-------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /home/ppaolino/research/projects/mlogit/polan/mlogit_replication.o
> ut
  log type:  text
 opened on:  10 Jul 2020, 11:20:47

.  set more off;

. /* input data from replication data set housed on JCR web-site */
> 
> use GelpiFeb2009;

. tab iraqwin warsucc, column;

+-------------------+
| Key               |
|-------------------|
|     frequency     |
| column percentage |
+-------------------+

  Iraq War |
      will | Iraq War Will Succeed
   succeed |         0          1 |     Total
-----------+----------------------+----------
         0 |       169          0 |       169 
           |     33.14       0.00 |     16.95 
-----------+----------------------+----------
         1 |       341          0 |       341 
           |     66.86       0.00 |     34.20 
-----------+----------------------+----------
         2 |         0        298 |       298 
           |      0.00      61.19 |     29.89 
-----------+----------------------+----------
         3 |         0        189 |       189 
           |      0.00      38.81 |     18.96 
-----------+----------------------+----------
     Total |       510        487 |       997 
           |    100.00     100.00 |    100.00 

. tab disapptimetable;

 Disapprove |
         of |
  Timetable |
        for |
 Withdrawal |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        480       48.24       48.24
          1 |        515       51.76      100.00
------------+-----------------------------------
      Total |        995      100.00

. /* now merge data from http://people.duke.edu/~gelpi/data.htm before
> any recoding has been done to show that the merge produces datasets
> that are from the same study */
> 
> /* Note, this set no longer available at Duke, but the zip file
> containing this file is listed (though not currently available) at
> http://politicalscience.osu.edu/faculty/gelpi.10/datasets.htm as
> GelpiPOCReplication.zip */
> 
> merge 1:1 caseid using GelpiPOC;

    Result                           # of obs.
    -----------------------------------------
    not matched                             0
    matched                             1,000  (_merge==3)
    -----------------------------------------

. tab iraqwin warsucc, column;

+-------------------+
| Key               |
|-------------------|
|     frequency     |
| column percentage |
+-------------------+

  Iraq War |
      will | Iraq War Will Succeed
   succeed |         0          1 |     Total
-----------+----------------------+----------
         0 |       169          0 |       169 
           |     33.14       0.00 |     16.95 
-----------+----------------------+----------
         1 |       341          0 |       341 
           |     66.86       0.00 |     34.20 
-----------+----------------------+----------
         2 |         0        298 |       298 
           |      0.00      61.19 |     29.89 
-----------+----------------------+----------
         3 |         0        189 |       189 
           |      0.00      38.81 |     18.96 
-----------+----------------------+----------
     Total |       510        487 |       997 
           |    100.00     100.00 |    100.00 

. tab approvetimetable;

Strongly or |
   Somewhat |
 Approve of |
a Timetable |
        for |
 Withdrawal |
  from Iraq |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        515       51.76       51.76
          1 |        480       48.24      100.00
------------+-----------------------------------
      Total |        995      100.00

. tab approvetimetable disapptimetable, column;

+-------------------+
| Key               |
|-------------------|
|     frequency     |
| column percentage |
+-------------------+

  Strongly |
        or |
  Somewhat |
Approve of |
         a |
 Timetable |     Disapprove of
       for |     Timetable for
Withdrawal |      Withdrawal
 from Iraq |         0          1 |     Total
-----------+----------------------+----------
         0 |         0        515 |       515 
           |      0.00     100.00 |     51.76 
-----------+----------------------+----------
         1 |       480          0 |       480 
           |    100.00       0.00 |     48.24 
-----------+----------------------+----------
     Total |       480        515 |       995 
           |    100.00     100.00 |    100.00 

. tab apptimetable approvetimetable, column;

+-------------------+
| Key               |
|-------------------|
|     frequency     |
| column percentage |
+-------------------+

           | Strongly or Somewhat
4 Category |     Approve of a
  Approval |     Timetable for
        of | Withdrawal from Iraq
 Timetable |         0          1 |     Total
-----------+----------------------+----------
         0 |       324          0 |       324 
           |     62.91       0.00 |     32.56 
-----------+----------------------+----------
         1 |       191          0 |       191 
           |     37.09       0.00 |     19.20 
-----------+----------------------+----------
         2 |         0        273 |       273 
           |      0.00      56.88 |     27.44 
-----------+----------------------+----------
         3 |         0        207 |       207 
           |      0.00      43.12 |     20.80 
-----------+----------------------+----------
     Total |       515        480 |       995 
           |    100.00     100.00 |    100.00 

. tab apptimetable disapptimetable, column;

+-------------------+
| Key               |
|-------------------|
|     frequency     |
| column percentage |
+-------------------+

4 Category |     Disapprove of
  Approval |     Timetable for
        of |      Withdrawal
 Timetable |         0          1 |     Total
-----------+----------------------+----------
         0 |         0        324 |       324 
           |      0.00      62.91 |     32.56 
-----------+----------------------+----------
         1 |         0        191 |       191 
           |      0.00      37.09 |     19.20 
-----------+----------------------+----------
         2 |       273          0 |       273 
           |     56.88       0.00 |     27.44 
-----------+----------------------+----------
         3 |       207          0 |       207 
           |     43.12       0.00 |     20.80 
-----------+----------------------+----------
     Total |       480        515 |       995 
           |    100.00     100.00 |    100.00 

. /* having done that, let's start from the beginning with the first
> task being using the data from the JCR web-site to replicate the
> published results */
> 
> gen disapptimetable4=3-apptimetable;
(5 missing values generated)

. tab disapptimetable4 apptimetable;

disapptime |      4 Category Approval of Timetable
    table4 |         0          1          2          3 |     Total
-----------+--------------------------------------------+----------
         0 |         0          0          0        207 |       207 
         1 |         0          0        273          0 |       273 
         2 |         0        191          0          0 |       191 
         3 |       324          0          0          0 |       324 
-----------+--------------------------------------------+----------
     Total |       324        191        273        207 |       995 

. tab disapptimetable apptimetable;

Disapprove |
        of |
 Timetable |
       for |      4 Category Approval of Timetable
Withdrawal |         0          1          2          3 |     Total
-----------+--------------------------------------------+----------
         0 |         0          0        273        207 |       480 
         1 |       324        191          0          0 |       515 
-----------+--------------------------------------------+----------
     Total |       324        191        273        207 |       995 

. tab disapptimetable disapptimetable4;

Disapprove |
        of |
 Timetable |
       for |              disapptimetable4
Withdrawal |         0          1          2          3 |     Total
-----------+--------------------------------------------+----------
         0 |       207        273          0          0 |       480 
         1 |         0          0        191        324 |       515 
-----------+--------------------------------------------+----------
     Total |       207        273        191        324 |       995 

. gen bushapp4=4-bushapprove;
(1 missing value generated)

. /* reproduce Gelpi's (2017) Withdrawal results from his Table 3 */ 
> /* results replicate exactly */
> 
> mlogit disapptimetable4 posevents negevents posbush negbush if 
> bushapp4==0, base(1);

Iteration 0:   log likelihood = -565.38364  
Iteration 1:   log likelihood = -556.81141  
Iteration 2:   log likelihood =  -556.6404  
Iteration 3:   log likelihood = -556.64005  
Iteration 4:   log likelihood = -556.64005  

Multinomial logistic regression                 Number of obs     =        450
                                                LR chi2(12)       =      17.49
                                                Prob > chi2       =     0.1322
Log likelihood = -556.64005                     Pseudo R2         =     0.0155

------------------------------------------------------------------------------
disapptime~4 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0            |
   posevents |   .4824653   .2798377     1.72   0.085    -.0660066    1.030937
   negevents |   .1832615   .2679417     0.68   0.494    -.3418945    .7084176
     posbush |   .1054397   .2662373     0.40   0.692    -.4163759    .6272553
     negbush |  -.0315451   .2757415    -0.11   0.909    -.5719886    .5088984
       _cons |  -.3451196   .2447674    -1.41   0.159    -.8248549    .1346158
-------------+----------------------------------------------------------------
1            |  (base outcome)
-------------+----------------------------------------------------------------
2            |
   posevents |    .248905   .3166647     0.79   0.432    -.3717464    .8695565
   negevents |  -.4623001   .3321772    -1.39   0.164    -1.113355    .1887552
     posbush |   .0337764   .3363465     0.10   0.920    -.6254507    .6930034
     negbush |   .4262153   .3216177     1.33   0.185    -.2041438    1.056574
       _cons |  -.7948333   .2877261    -2.76   0.006    -1.358766   -.2309004
-------------+----------------------------------------------------------------
3            |
   posevents |   1.235248    .491081     2.52   0.012     .2727465    2.197749
   negevents |   .6378964   .5025445     1.27   0.204    -.3470728    1.622866
     posbush |   .3806396   .4272345     0.89   0.373    -.4567246    1.218004
     negbush |  -.1981745   .4829264    -0.41   0.682    -1.144693    .7483439
       _cons |  -2.306583   .4760887    -4.84   0.000    -3.239699   -1.373466
------------------------------------------------------------------------------

. mlogit disapptimetable4 posevents negevents posbush negbush if 
> bushapp4==1, base(1);

Iteration 0:   log likelihood = -196.04164  
Iteration 1:   log likelihood = -192.77426  
Iteration 2:   log likelihood =  -192.7627  
Iteration 3:   log likelihood =  -192.7627  

Multinomial logistic regression                 Number of obs     =        145
                                                LR chi2(12)       =       6.56
                                                Prob > chi2       =     0.8854
Log likelihood =  -192.7627                     Pseudo R2         =     0.0167

------------------------------------------------------------------------------
disapptime~4 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0            |
   posevents |   .1978713    .621897     0.32   0.750    -1.021024    1.416767
   negevents |   .4594221    .563948     0.81   0.415    -.6458957     1.56474
     posbush |  -.6714674   .6111686    -1.10   0.272    -1.869336    .5264011
     negbush |  -.3511409   .5985308    -0.59   0.557     -1.52424    .8219579
       _cons |  -.3363287   .4805881    -0.70   0.484    -1.278264    .6056067
-------------+----------------------------------------------------------------
1            |  (base outcome)
-------------+----------------------------------------------------------------
2            |
   posevents |   .8946703   .6296942     1.42   0.155    -.3395077    2.128848
   negevents |   .5742642   .6252596     0.92   0.358    -.6512221     1.79975
     posbush |  -.7673819   .6170079    -1.24   0.214    -1.976695    .4419314
     negbush |   -.930913   .6462991    -1.44   0.150    -2.197636      .33581
       _cons |  -.5139592    .506665    -1.01   0.310    -1.507004     .479086
-------------+----------------------------------------------------------------
3            |
   posevents |   .7826585   .5291161     1.48   0.139    -.2543899    1.819707
   negevents |   .3819931   .5269622     0.72   0.469    -.6508338     1.41482
     posbush |  -.6585469   .5337647    -1.23   0.217    -1.704706    .3876127
     negbush |  -.6697131   .5481613    -1.22   0.222    -1.744089    .4046633
       _cons |   .0382077   .4336582     0.09   0.930    -.8117469    .8881622
------------------------------------------------------------------------------

. mlogit disapptimetable4 posevents negevents posbush negbush if 
> bushapp4==2, base(1);

Iteration 0:   log likelihood =  -311.6775  
Iteration 1:   log likelihood = -303.55171  
Iteration 2:   log likelihood = -303.33647  
Iteration 3:   log likelihood =  -303.3358  
Iteration 4:   log likelihood =  -303.3358  

Multinomial logistic regression                 Number of obs     =        281
                                                LR chi2(12)       =      16.68
                                                Prob > chi2       =     0.1619
Log likelihood =  -303.3358                     Pseudo R2         =     0.0268

------------------------------------------------------------------------------
disapptime~4 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0            |
   posevents |   .0616675   .8584677     0.07   0.943    -1.620898    1.744233
   negevents |   .6932904   .7803337     0.89   0.374    -.8361355    2.222716
     posbush |  -.3054506   .8368452    -0.37   0.715    -1.945637    1.334736
     negbush |   .0409455   .6899031     0.06   0.953     -1.31124    1.393131
       _cons |  -1.334224   .7500554    -1.78   0.075    -2.804305    .1358579
-------------+----------------------------------------------------------------
1            |  (base outcome)
-------------+----------------------------------------------------------------
2            |
   posevents |  -.3704563   .4806628    -0.77   0.441    -1.312538    .5716254
   negevents |  -.9128674   .5024815    -1.82   0.069    -1.897713    .0719782
     posbush |   .5614278   .5258975     1.07   0.286    -.4693123    1.592168
     negbush |   .4115443   .4783918     0.86   0.390    -.5260864    1.349175
       _cons |   .4946566   .4508538     1.10   0.273    -.3890007    1.378314
-------------+----------------------------------------------------------------
3            |
   posevents |   .2616708   .4372636     0.60   0.550    -.5953501    1.118692
   negevents |  -.0997474   .4389767    -0.23   0.820    -.9601259    .7606312
     posbush |   .0765542   .4477232     0.17   0.864    -.8009672    .9540757
     negbush |  -.4619053   .4081863    -1.13   0.258    -1.261936    .3381252
       _cons |   1.373296   .3951206     3.48   0.001     .5988743    2.147719
------------------------------------------------------------------------------

. mlogit disapptimetable4 posevents negevents posbush negbush if 
> bushapp4==3, base(1);

Iteration 0:   log likelihood = -103.25873  
Iteration 1:   log likelihood = -95.257351  
Iteration 2:   log likelihood = -94.008604  
Iteration 3:   log likelihood = -93.817061  
Iteration 4:   log likelihood = -93.769697  
Iteration 5:   log likelihood = -93.759919  
Iteration 6:   log likelihood = -93.757821  
Iteration 7:   log likelihood = -93.757352  
Iteration 8:   log likelihood = -93.757236  
Iteration 9:   log likelihood = -93.757213  
Iteration 10:  log likelihood = -93.757208  

Multinomial logistic regression                 Number of obs     =        118
                                                LR chi2(12)       =      19.00
                                                Prob > chi2       =     0.0885
Log likelihood = -93.757208                     Pseudo R2         =     0.0920

------------------------------------------------------------------------------
disapptime~4 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0            |
   posevents |  -1.976839   1.295792    -1.53   0.127    -4.516544    .5628668
   negevents |   -2.10056   1.304011    -1.61   0.107    -4.656375    .4552546
     posbush |   15.75549   873.1916     0.02   0.986    -1695.669     1727.18
     negbush |   .9424272   1.102402     0.85   0.393    -1.218242    3.103096
       _cons |   .3146791   .9440761     0.33   0.739    -1.535676    2.165034
-------------+----------------------------------------------------------------
1            |  (base outcome)
-------------+----------------------------------------------------------------
2            |
   posevents |  -2.247635    1.19049    -1.89   0.059    -4.580952    .0856823
   negevents |  -1.780507   1.132358    -1.57   0.116    -3.999888    .4388744
     posbush |   16.50042   873.1916     0.02   0.985    -1694.924    1727.924
     negbush |   2.444964   1.072278     2.28   0.023     .3433378    4.546591
       _cons |  -.1077583    1.02057    -0.11   0.916    -2.108039    1.892523
-------------+----------------------------------------------------------------
3            |
   posevents |  -.8937606   .9411479    -0.95   0.342    -2.738377    .9508554
   negevents |  -.9214935    .934176    -0.99   0.324    -2.752445    .9094577
     posbush |   15.14715   873.1911     0.02   0.986    -1696.276     1726.57
     negbush |   .2562956   .7179463     0.36   0.721    -1.150853    1.663444
       _cons |   2.391246   .7498763     3.19   0.001     .9215159    3.860977
------------------------------------------------------------------------------

. /* Reproduce Gelpi's (2017) Withdrawal results from his Figure 3 */ 
> /* results replicate approximately.  */
> 
> estsimp mlogit disapptimetable4 posevents negevents posbush negbush if 
> bushapp4==0, base(1);

Iteration 0:   log likelihood = -565.38364
Iteration 1:   log likelihood = -556.81141
Iteration 2:   log likelihood =  -556.6404
Iteration 3:   log likelihood = -556.64005

Multinomial logistic regression                   Number of obs   =        450
                                                  LR chi2(12)     =      17.49
                                                  Prob > chi2     =     0.1322
Log likelihood = -556.64005                       Pseudo R2       =     0.0155

------------------------------------------------------------------------------
disapptime~4 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0            |
   posevents |   .4824653   .2798379     1.72   0.085    -.0660069    1.030937
   negevents |   .1832615   .2679418     0.68   0.494    -.3418948    .7084179
     posbush |   .1054397   .2662374     0.40   0.692    -.4163761    .6272555
     negbush |  -.0315451   .2757416    -0.11   0.909    -.5719887    .5088985
       _cons |  -.3451196   .2447676    -1.41   0.159    -.8248553    .1346161
-------------+----------------------------------------------------------------
2            |
   posevents |    .248905   .3166649     0.79   0.432    -.3717467    .8695567
   negevents |  -.4623001   .3321773    -1.39   0.164    -1.113356    .1887554
     posbush |   .0337764   .3363466     0.10   0.920    -.6254509    .6930037
     negbush |   .4262153   .3216178     1.33   0.185    -.2041439    1.056575
       _cons |  -.7948333   .2877263    -2.76   0.006    -1.358766   -.2309001
-------------+----------------------------------------------------------------
3            |
   posevents |   1.235248   .4910753     2.52   0.012     .2727576    2.197737
   negevents |   .6378964   .5025391     1.27   0.204    -.3470621    1.622855
     posbush |   .3806396   .4272331     0.89   0.373    -.4567218    1.218001
     negbush |  -.1981745   .4829247    -0.41   0.682     -1.14469    .7483406
       _cons |  -2.306583   .4760825    -4.84   0.000    -3.239687   -1.373478
------------------------------------------------------------------------------
(disapptimetable4==1 is the base outcome)

Simulating main parameters.  Please wait....
% of simulations completed: 6% 13% 20% 26% 33% 40% 46% 53% 60% 66% 73% 80% 86% 
> 93% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13 b14 b15

. setx 0;

. simqi, fd(prval(0))  changex(posevents 0 1);

First Difference: posevents 0 1

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(disapp~4 = 0) |    .056666     .0548319     -.049786    .1697237

. simqi, fd(prval(1))  changex(posevents 0 1);

First Difference: posevents 0 1

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(disapp~4 = 1) |  -.1149161     .0562103    -.2264545   -.0066978

. simqi, fd(prval(2))  changex(posevents 0 1);

First Difference: posevents 0 1

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(disapp~4 = 2) |  -.0104295     .0441167    -.0948666    .0791084

. simqi, fd(prval(3))  changex(posevents 0 1);

First Difference: posevents 0 1

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(disapp~4 = 3) |   .0686795     .0358919     .0067345    .1491013

. drop b1-b15;

. estsimp mlogit disapptimetable4 posevents negevents posbush negbush if
> bushapp4==1, base(1);

Iteration 0:   log likelihood = -196.04164
Iteration 1:   log likelihood = -192.77426
Iteration 2:   log likelihood =  -192.7627
Iteration 3:   log likelihood =  -192.7627

Multinomial logistic regression                   Number of obs   =        145
                                                  LR chi2(12)     =       6.56
                                                  Prob > chi2     =     0.8854
Log likelihood =  -192.7627                       Pseudo R2       =     0.0167

------------------------------------------------------------------------------
disapptime~4 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0            |
   posevents |   .1978714    .621897     0.32   0.750    -1.021024    1.416767
   negevents |   .4594222    .563948     0.81   0.415    -.6458957     1.56474
     posbush |  -.6714674   .6111686    -1.10   0.272    -1.869336    .5264011
     negbush |  -.3511409   .5985308    -0.59   0.557     -1.52424    .8219579
       _cons |  -.3363287   .4805881    -0.70   0.484    -1.278264    .6056067
-------------+----------------------------------------------------------------
2            |
   posevents |   .8946703   .6296942     1.42   0.155    -.3395077    2.128848
   negevents |   .5742642   .6252596     0.92   0.358    -.6512221    1.799751
     posbush |  -.7673819   .6170079    -1.24   0.214    -1.976695    .4419314
     negbush |  -.9309131   .6462991    -1.44   0.150    -2.197636      .33581
       _cons |  -.5139592    .506665    -1.01   0.310    -1.507004     .479086
-------------+----------------------------------------------------------------
3            |
   posevents |   .7826585   .5291161     1.48   0.139    -.2543899    1.819707
   negevents |   .3819931   .5269622     0.72   0.469    -.6508338     1.41482
     posbush |  -.6585469   .5337647    -1.23   0.217    -1.704706    .3876126
     negbush |  -.6697131   .5481613    -1.22   0.222    -1.744089    .4046632
       _cons |   .0382077   .4336582     0.09   0.930    -.8117468    .8881622
------------------------------------------------------------------------------
(disapptimetable4==1 is the base outcome)

Simulating main parameters.  Please wait....
% of simulations completed: 6% 13% 20% 26% 33% 40% 46% 53% 60% 66% 73% 80% 86% 
> 93% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13 b14 b15

. setx 0;

. simqi, fd(prval(0))  changex(posevents 0 1);

First Difference: posevents 0 1

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(disapp~4 = 0) |  -.0530504     .0801977    -.2062207    .1085352

. simqi, fd(prval(1))  changex(posevents 0 1);

First Difference: posevents 0 1

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(disapp~4 = 1) |   -.115836       .07615    -.2649135    .0346864

. simqi, fd(prval(2))  changex(posevents 0 1);

First Difference: posevents 0 1

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(disapp~4 = 2) |   .0797232     .1007483    -.1018042    .2985649

. simqi, fd(prval(3))  changex(posevents 0 1);

First Difference: posevents 0 1

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(disapp~4 = 3) |   .0891631      .104469    -.1041093    .3058261

. drop b1-b15;

. estsimp mlogit disapptimetable4 posevents negevents posbush negbush if
> bushapp4==2, base(1);

Iteration 0:   log likelihood =  -311.6775
Iteration 1:   log likelihood = -303.55171
Iteration 2:   log likelihood = -303.33647
Iteration 3:   log likelihood =  -303.3358
Iteration 4:   log likelihood =  -303.3358

Multinomial logistic regression                   Number of obs   =        281
                                                  LR chi2(12)     =      16.68
                                                  Prob > chi2     =     0.1619
Log likelihood =  -303.3358                       Pseudo R2       =     0.0268

------------------------------------------------------------------------------
disapptime~4 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0            |
   posevents |   .0616675   .8584677     0.07   0.943    -1.620898    1.744233
   negevents |   .6932904   .7803337     0.89   0.374    -.8361355    2.222716
     posbush |  -.3054506   .8368452    -0.37   0.715    -1.945637    1.334736
     negbush |   .0409455   .6899031     0.06   0.953     -1.31124    1.393131
       _cons |  -1.334224   .7500554    -1.78   0.075    -2.804305    .1358579
-------------+----------------------------------------------------------------
2            |
   posevents |  -.3704563   .4806628    -0.77   0.441    -1.312538    .5716254
   negevents |  -.9128674   .5024815    -1.82   0.069    -1.897713    .0719782
     posbush |   .5614278   .5258975     1.07   0.286    -.4693123    1.592168
     negbush |   .4115443   .4783918     0.86   0.390    -.5260864    1.349175
       _cons |   .4946566   .4508538     1.10   0.273    -.3890007    1.378314
-------------+----------------------------------------------------------------
3            |
   posevents |   .2616708   .4372636     0.60   0.550    -.5953501    1.118692
   negevents |  -.0997474   .4389767    -0.23   0.820    -.9601259    .7606312
     posbush |   .0765542   .4477232     0.17   0.864    -.8009672    .9540757
     negbush |  -.4619053   .4081863    -1.13   0.258    -1.261936    .3381252
       _cons |   1.373296   .3951206     3.48   0.001     .5988743    2.147719
------------------------------------------------------------------------------
(disapptimetable4==1 is the base outcome)

Simulating main parameters.  Please wait....
% of simulations completed: 6% 13% 20% 26% 33% 40% 46% 53% 60% 66% 73% 80% 86% 
> 93% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13 b14 b15

. setx 0;

. simqi, fd(prval(0))  changex(negevents 0 1);

First Difference: negevents 0 1

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(disapp~4 = 0) |    .053886     .0498487    -.0250093    .1762556

. simqi, fd(prval(1))  changex(negevents 0 1);

First Difference: negevents 0 1

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(disapp~4 = 1) |   .0259078     .0564552     -.089936    .1338263

. simqi, fd(prval(2))  changex(negevents 0 1);

First Difference: negevents 0 1

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(disapp~4 = 2) |  -.1226348     .0560601    -.2428892   -.0194291

. simqi, fd(prval(3))  changex(negevents 0 1);

First Difference: negevents 0 1

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(disapp~4 = 3) |   .0428411     .0786799    -.1177712     .191159

. drop b1-b15;

. estsimp mlogit disapptimetable4 posevents negevents posbush negbush if
> bushapp4==3, base(1);

Iteration 0:   log likelihood = -103.25873
Iteration 1:   log likelihood = -95.257351
Iteration 2:   log likelihood = -94.084144
Iteration 3:   log likelihood = -93.873112
Iteration 4:   log likelihood = -93.799714
Iteration 5:   log likelihood = -93.772827
Iteration 6:   log likelihood =  -93.76295
Iteration 7:   log likelihood = -93.759319
Iteration 8:   log likelihood = -93.757983
Iteration 9:   log likelihood = -93.757492
Iteration 10:  log likelihood = -93.757311
Iteration 11:  log likelihood = -93.757245
Iteration 12:  log likelihood =  -93.75722
Iteration 13:  log likelihood = -93.757211
Iteration 14:  log likelihood = -93.757208
Iteration 15:  log likelihood = -93.757207
Iteration 16:  log likelihood = -93.757206
Iteration 17:  log likelihood = -93.757206
Iteration 18:  log likelihood = -93.757206
Iteration 19:  log likelihood = -93.757206
Iteration 20:  log likelihood = -93.757206
Iteration 21:  log likelihood = -93.757206

Multinomial logistic regression                   Number of obs   =        118
                                                  LR chi2(12)     =      19.00
                                                  Prob > chi2     =     0.0885
Log likelihood = -93.757206                       Pseudo R2       =     0.0920

------------------------------------------------------------------------------
disapptime~4 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0            |
   posevents |   -1.97684   1.295756    -1.53   0.127    -4.516474    .5627948
   negevents |   -2.10053    1.30397    -1.61   0.107    -4.656265    .4552048
     posbush |   22.37101          .        .       .            .           .
     negbush |    .942404   1.102376     0.85   0.393    -1.218214    3.103022
       _cons |   .3146548   .9440306     0.33   0.739    -1.535611    2.164921
-------------+----------------------------------------------------------------
2            |
   posevents |  -2.247584   1.190436    -1.89   0.059    -4.580795    .0856269
   negevents |  -1.780477   1.132312    -1.57   0.116    -3.999769    .4388141
     posbush |   23.11589   1.388753    16.65   0.000     20.39398    25.83779
     negbush |   2.445012   1.072227     2.28   0.023     .3434857    4.546538
       _cons |  -.1077445    1.02051    -0.11   0.916    -2.107907    1.892418
-------------+----------------------------------------------------------------
3            |
   posevents |  -.8937428    .941112    -0.95   0.342    -2.738288    .9508028
   negevents |  -.9214712   .9341408    -0.99   0.324    -2.752354    .9094112
     posbush |   21.76276   1.052521    20.68   0.000     19.69986    23.82566
     negbush |   .2563456   .7179289     0.36   0.721    -1.150769     1.66346
       _cons |   2.391096   .7498417     3.19   0.001     .9214338    3.860759
------------------------------------------------------------------------------
(disapptimetable4==1 is the base outcome)

Simulating main parameters.  Please wait....
% of simulations completed: 6% 13% 20% 26% 33% 40% 46% 53% 60% 66% 73% 80% 86% 
> 93% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13 b14 b15

. setx 0;

. simqi, fd(prval(0))  changex(posevents 0 1);

First Difference: posevents 0 1

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(disapp~4 = 0) |  -.0567893     .0679284    -.2042019    .0892255

. simqi, fd(prval(1))  changex(posevents 0 1);

First Difference: posevents 0 1

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(disapp~4 = 1) |   .1019962     .1034577    -.0922141    .3357071

. simqi, fd(prval(2))  changex(posevents 0 1);

First Difference: posevents 0 1

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(disapp~4 = 2) |  -.0524836     .0470765    -.1753328      .00285

. simqi, fd(prval(3))  changex(posevents 0 1);

First Difference: posevents 0 1

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(disapp~4 = 3) |   .0072767     .1185765    -.2382891    .2289973

. simqi, fd(prval(0))  changex(negbush 0 1);

First Difference: negbush 0 1

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(disapp~4 = 0) |   .0298105     .0987372    -.1482546    .2693458

. simqi, fd(prval(1))  changex(negbush 0 1);

First Difference: negbush 0 1

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(disapp~4 = 1) |  -.0382039      .049478    -.1490916    .0423891

. simqi, fd(prval(2))  changex(negbush 0 1);

First Difference: negbush 0 1

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(disapp~4 = 2) |   .2788283       .13274     .0504931    .5637696

. simqi, fd(prval(3))  changex(negbush 0 1);

First Difference: negbush 0 1

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(disapp~4 = 3) |  -.2704349     .1310776      -.52698   -.0156945

. /* Reproduce results from my Table 1 */
> 
> mlogit disapptimetable4 i.posevents i.negevents i.posbush i.negbush if
> bushapp4==0, base(1);

Iteration 0:   log likelihood = -565.38364  
Iteration 1:   log likelihood = -556.81141  
Iteration 2:   log likelihood =  -556.6404  
Iteration 3:   log likelihood = -556.64005  
Iteration 4:   log likelihood = -556.64005  

Multinomial logistic regression                 Number of obs     =        450
                                                LR chi2(12)       =      17.49
                                                Prob > chi2       =     0.1322
Log likelihood = -556.64005                     Pseudo R2         =     0.0155

------------------------------------------------------------------------------
disapptime~4 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0            |
 1.posevents |   .4824653   .2798377     1.72   0.085    -.0660066    1.030937
 1.negevents |   .1832615   .2679417     0.68   0.494    -.3418945    .7084176
   1.posbush |   .1054397   .2662373     0.40   0.692    -.4163759    .6272553
   1.negbush |  -.0315451   .2757415    -0.11   0.909    -.5719886    .5088984
       _cons |  -.3451196   .2447674    -1.41   0.159    -.8248549    .1346158
-------------+----------------------------------------------------------------
1            |  (base outcome)
-------------+----------------------------------------------------------------
2            |
 1.posevents |    .248905   .3166647     0.79   0.432    -.3717464    .8695565
 1.negevents |  -.4623001   .3321772    -1.39   0.164    -1.113355    .1887552
   1.posbush |   .0337764   .3363465     0.10   0.920    -.6254507    .6930034
   1.negbush |   .4262153   .3216177     1.33   0.185    -.2041438    1.056574
       _cons |  -.7948333   .2877261    -2.76   0.006    -1.358766   -.2309004
-------------+----------------------------------------------------------------
3            |
 1.posevents |   1.235248    .491081     2.52   0.012     .2727465    2.197749
 1.negevents |   .6378964   .5025445     1.27   0.204    -.3470728    1.622866
   1.posbush |   .3806396   .4272345     0.89   0.373    -.4567246    1.218004
   1.negbush |  -.1981745   .4829264    -0.41   0.682    -1.144693    .7483439
       _cons |  -2.306583   .4760887    -4.84   0.000    -3.239699   -1.373466
------------------------------------------------------------------------------

. margins r.posevents, at(negevents=0 posbush=0 negbush=0);

Contrasts of adjusted predictions
Model VCE    : OIM

1._predict   : Pr(disapptimetable4==0), predict(pr outcome(0))
2._predict   : Pr(disapptimetable4==1), predict(pr outcome(1))
3._predict   : Pr(disapptimetable4==2), predict(pr outcome(2))
4._predict   : Pr(disapptimetable4==3), predict(pr outcome(3))
at           : negevents       =           0
               posbush         =           0
               negbush         =           0

------------------------------------------------------
                   |         df        chi2     P>chi2
-------------------+----------------------------------
posevents@_predict |
       (1 vs 0) 1  |          1        1.16     0.2808
       (1 vs 0) 2  |          1        4.20     0.0405
       (1 vs 0) 3  |          1        0.06     0.8020
       (1 vs 0) 4  |          1        3.98     0.0461
            Joint  |          3        7.13     0.0678
------------------------------------------------------

--------------------------------------------------------------------
                   |            Delta-method
                   |   Contrast   Std. Err.     [95% Conf. Interval]
-------------------+------------------------------------------------
posevents@_predict |
       (1 vs 0) 1  |   .0603824   .0559811     -.0493386    .1701034
       (1 vs 0) 2  |  -.1167661   .0569842     -.2284531   -.0050791
       (1 vs 0) 3  |  -.0111468   .0444517     -.0982705     .075977
       (1 vs 0) 4  |   .0675305   .0338637      .0011589    .1339021
--------------------------------------------------------------------

. matrix pe01=r(table)';

. matrix pe0=pe01[1..4,1..2];

. margins r.negbush, at(posevents=0 negevents=0 posbush=0);

Contrasts of adjusted predictions
Model VCE    : OIM

1._predict   : Pr(disapptimetable4==0), predict(pr outcome(0))
2._predict   : Pr(disapptimetable4==1), predict(pr outcome(1))
3._predict   : Pr(disapptimetable4==2), predict(pr outcome(2))
4._predict   : Pr(disapptimetable4==3), predict(pr outcome(3))
at           : posevents       =           0
               negevents       =           0
               posbush         =           0

----------------------------------------------------
                 |         df        chi2     P>chi2
-----------------+----------------------------------
negbush@_predict |
     (1 vs 0) 1  |          1        0.44     0.5057
     (1 vs 0) 2  |          1        0.37     0.5436
     (1 vs 0) 3  |          1        2.31     0.1287
     (1 vs 0) 4  |          1        0.40     0.5274
          Joint  |          3        2.58     0.4618
----------------------------------------------------

------------------------------------------------------------------
                 |            Delta-method
                 |   Contrast   Std. Err.     [95% Conf. Interval]
-----------------+------------------------------------------------
negbush@_predict |
     (1 vs 0) 1  |  -.0344442   .0517484     -.1358692    .0669809
     (1 vs 0) 2  |  -.0360151   .0593009     -.1522428    .0802125
     (1 vs 0) 3  |   .0813265   .0535338     -.0235977    .1862508
     (1 vs 0) 4  |  -.0108672   .0171972     -.0445731    .0228386
------------------------------------------------------------------

. matrix nb01=r(table)';

. matrix nb0=nb01[1..4,1..2];

. matrix ob01=r(_N);

. matrix ob0=ob01[1,1..2];

. mlogit disapptimetable4 i.posevents i.negevents i.posbush i.negbush if
> bushapp4==1, base(1);

Iteration 0:   log likelihood = -196.04164  
Iteration 1:   log likelihood = -192.77426  
Iteration 2:   log likelihood =  -192.7627  
Iteration 3:   log likelihood =  -192.7627  

Multinomial logistic regression                 Number of obs     =        145
                                                LR chi2(12)       =       6.56
                                                Prob > chi2       =     0.8854
Log likelihood =  -192.7627                     Pseudo R2         =     0.0167

------------------------------------------------------------------------------
disapptime~4 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0            |
 1.posevents |   .1978713    .621897     0.32   0.750    -1.021024    1.416767
 1.negevents |   .4594221    .563948     0.81   0.415    -.6458957     1.56474
   1.posbush |  -.6714674   .6111686    -1.10   0.272    -1.869336    .5264011
   1.negbush |  -.3511409   .5985308    -0.59   0.557     -1.52424    .8219579
       _cons |  -.3363287   .4805881    -0.70   0.484    -1.278264    .6056067
-------------+----------------------------------------------------------------
1            |  (base outcome)
-------------+----------------------------------------------------------------
2            |
 1.posevents |   .8946703   .6296942     1.42   0.155    -.3395077    2.128848
 1.negevents |   .5742642   .6252596     0.92   0.358    -.6512221     1.79975
   1.posbush |  -.7673819   .6170079    -1.24   0.214    -1.976695    .4419314
   1.negbush |   -.930913   .6462991    -1.44   0.150    -2.197636      .33581
       _cons |  -.5139592    .506665    -1.01   0.310    -1.507004     .479086
-------------+----------------------------------------------------------------
3            |
 1.posevents |   .7826585   .5291161     1.48   0.139    -.2543899    1.819707
 1.negevents |   .3819931   .5269622     0.72   0.469    -.6508338     1.41482
   1.posbush |  -.6585469   .5337647    -1.23   0.217    -1.704706    .3876127
   1.negbush |  -.6697131   .5481613    -1.22   0.222    -1.744089    .4046633
       _cons |   .0382077   .4336582     0.09   0.930    -.8117469    .8881622
------------------------------------------------------------------------------

. margins r.posevents, at(negevents=0 posbush=0 negbush=0);

Contrasts of adjusted predictions
Model VCE    : OIM

1._predict   : Pr(disapptimetable4==0), predict(pr outcome(0))
2._predict   : Pr(disapptimetable4==1), predict(pr outcome(1))
3._predict   : Pr(disapptimetable4==2), predict(pr outcome(2))
4._predict   : Pr(disapptimetable4==3), predict(pr outcome(3))
at           : negevents       =           0
               posbush         =           0
               negbush         =           0

------------------------------------------------------
                   |         df        chi2     P>chi2
-------------------+----------------------------------
posevents@_predict |
       (1 vs 0) 1  |          1        0.53     0.4684
       (1 vs 0) 2  |          1        2.37     0.1236
       (1 vs 0) 3  |          1        0.70     0.4033
       (1 vs 0) 4  |          1        0.80     0.3698
            Joint  |          3        3.50     0.3214
------------------------------------------------------

--------------------------------------------------------------------
                   |            Delta-method
                   |   Contrast   Std. Err.     [95% Conf. Interval]
-------------------+------------------------------------------------
posevents@_predict |
       (1 vs 0) 1  |  -.0578553   .0797956     -.2142517    .0985411
       (1 vs 0) 2  |   -.120013    .077935     -.2727628    .0327369
       (1 vs 0) 3  |   .0825389   .0987705     -.1110477    .2761256
       (1 vs 0) 4  |   .0953293    .106296      -.113007    .3036656
--------------------------------------------------------------------

. matrix pe11=r(table)';

. matrix pe1=pe11[1..4,1..2];

. margins r.negbush, at(posevents=0 negevents=0 posbush=0);

Contrasts of adjusted predictions
Model VCE    : OIM

1._predict   : Pr(disapptimetable4==0), predict(pr outcome(0))
2._predict   : Pr(disapptimetable4==1), predict(pr outcome(1))
3._predict   : Pr(disapptimetable4==2), predict(pr outcome(2))
4._predict   : Pr(disapptimetable4==3), predict(pr outcome(3))
at           : posevents       =           0
               negevents       =           0
               posbush         =           0

----------------------------------------------------
                 |         df        chi2     P>chi2
-----------------+----------------------------------
negbush@_predict |
     (1 vs 0) 1  |          1        0.01     0.9248
     (1 vs 0) 2  |          1        1.70     0.1919
     (1 vs 0) 3  |          1        1.31     0.2520
     (1 vs 0) 4  |          1        0.72     0.3961
          Joint  |          3        2.64     0.4498
----------------------------------------------------

------------------------------------------------------------------
                 |            Delta-method
                 |   Contrast   Std. Err.     [95% Conf. Interval]
-----------------+------------------------------------------------
negbush@_predict |
     (1 vs 0) 1  |   .0083206   .0881134     -.1643784    .1810197
     (1 vs 0) 2  |   .1420708    .108858     -.0712869    .3554285
     (1 vs 0) 3  |  -.0746192    .065136     -.2022835    .0530451
     (1 vs 0) 4  |  -.0757722   .0892818     -.2507612    .0992168
------------------------------------------------------------------

. matrix nb11=r(table)';

. matrix nb1=nb11[1..4,1..2];

. matrix ob11=r(_N);

. matrix ob1=ob11[1,1..2];

. mlogit disapptimetable4 i.posevents i.negevents i.posbush i.negbush if
> bushapp4==2, base(1);

Iteration 0:   log likelihood =  -311.6775  
Iteration 1:   log likelihood = -303.55171  
Iteration 2:   log likelihood = -303.33647  
Iteration 3:   log likelihood =  -303.3358  
Iteration 4:   log likelihood =  -303.3358  

Multinomial logistic regression                 Number of obs     =        281
                                                LR chi2(12)       =      16.68
                                                Prob > chi2       =     0.1619
Log likelihood =  -303.3358                     Pseudo R2         =     0.0268

------------------------------------------------------------------------------
disapptime~4 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0            |
 1.posevents |   .0616675   .8584677     0.07   0.943    -1.620898    1.744233
 1.negevents |   .6932904   .7803337     0.89   0.374    -.8361355    2.222716
   1.posbush |  -.3054506   .8368452    -0.37   0.715    -1.945637    1.334736
   1.negbush |   .0409455   .6899031     0.06   0.953     -1.31124    1.393131
       _cons |  -1.334224   .7500554    -1.78   0.075    -2.804305    .1358579
-------------+----------------------------------------------------------------
1            |  (base outcome)
-------------+----------------------------------------------------------------
2            |
 1.posevents |  -.3704563   .4806628    -0.77   0.441    -1.312538    .5716254
 1.negevents |  -.9128674   .5024815    -1.82   0.069    -1.897713    .0719782
   1.posbush |   .5614278   .5258975     1.07   0.286    -.4693123    1.592168
   1.negbush |   .4115443   .4783918     0.86   0.390    -.5260864    1.349175
       _cons |   .4946566   .4508538     1.10   0.273    -.3890007    1.378314
-------------+----------------------------------------------------------------
3            |
 1.posevents |   .2616708   .4372636     0.60   0.550    -.5953501    1.118692
 1.negevents |  -.0997474   .4389767    -0.23   0.820    -.9601259    .7606312
   1.posbush |   .0765542   .4477232     0.17   0.864    -.8009672    .9540757
   1.negbush |  -.4619053   .4081863    -1.13   0.258    -1.261936    .3381252
       _cons |   1.373296   .3951206     3.48   0.001     .5988743    2.147719
------------------------------------------------------------------------------

. margins r.posevents, at(negevents=0 posbush=0 negbush=0);

Contrasts of adjusted predictions
Model VCE    : OIM

1._predict   : Pr(disapptimetable4==0), predict(pr outcome(0))
2._predict   : Pr(disapptimetable4==1), predict(pr outcome(1))
3._predict   : Pr(disapptimetable4==2), predict(pr outcome(2))
4._predict   : Pr(disapptimetable4==3), predict(pr outcome(3))
at           : negevents       =           0
               posbush         =           0
               negbush         =           0

------------------------------------------------------
                   |         df        chi2     P>chi2
-------------------+----------------------------------
posevents@_predict |
       (1 vs 0) 1  |          1        0.00     0.9637
       (1 vs 0) 2  |          1        0.07     0.7922
       (1 vs 0) 3  |          1        2.83     0.0926
       (1 vs 0) 4  |          1        2.21     0.1369
            Joint  |          3        3.17     0.3658
------------------------------------------------------

--------------------------------------------------------------------
                   |            Delta-method
                   |   Contrast   Std. Err.     [95% Conf. Interval]
-------------------+------------------------------------------------
posevents@_predict |
       (1 vs 0) 1  |  -.0012955   .0284606     -.0570773    .0544863
       (1 vs 0) 2  |  -.0133534   .0506835     -.1126912    .0859844
       (1 vs 0) 3  |  -.0892172   .0530496     -.1931925    .0147582
       (1 vs 0) 4  |   .1038661   .0698204     -.0329793    .2407114
--------------------------------------------------------------------

. matrix pe21=r(table)';

. matrix pe2=pe21[1..4,1..2];

. margins r.negbush, at(posevents=0 negevents=0 posbush=0);

Contrasts of adjusted predictions
Model VCE    : OIM

1._predict   : Pr(disapptimetable4==0), predict(pr outcome(0))
2._predict   : Pr(disapptimetable4==1), predict(pr outcome(1))
3._predict   : Pr(disapptimetable4==2), predict(pr outcome(2))
4._predict   : Pr(disapptimetable4==3), predict(pr outcome(3))
at           : posevents       =           0
               negevents       =           0
               posbush         =           0

----------------------------------------------------
                 |         df        chi2     P>chi2
-----------------+----------------------------------
negbush@_predict |
     (1 vs 0) 1  |          1        0.05     0.8199
     (1 vs 0) 2  |          1        0.08     0.7796
     (1 vs 0) 3  |          1        4.31     0.0379
     (1 vs 0) 4  |          1        5.96     0.0146
          Joint  |          3        6.20     0.1024
----------------------------------------------------

------------------------------------------------------------------
                 |            Delta-method
                 |   Contrast   Std. Err.     [95% Conf. Interval]
-----------------+------------------------------------------------
negbush@_predict |
     (1 vs 0) 1  |   .0055524   .0243841     -.0422395    .0533443
     (1 vs 0) 2  |   .0143817   .0513911      -.086343    .1151065
     (1 vs 0) 3  |   .1574572   .0758558      .0087825    .3061318
     (1 vs 0) 4  |  -.1773913   .0726715     -.3198248   -.0349578
------------------------------------------------------------------

. matrix nb21=r(table)';

. matrix nb2=nb21[1..4,1..2];

. matrix ob21=r(_N);

. matrix ob2=ob21[1,1..2];

. mlogit disapptimetable4 i.posevents i.negevents i.posbush i.negbush if
> bushapp4==3, base(1);

Iteration 0:   log likelihood = -103.25873  
Iteration 1:   log likelihood = -95.257351  
Iteration 2:   log likelihood = -94.008604  
Iteration 3:   log likelihood = -93.817061  
Iteration 4:   log likelihood = -93.769697  
Iteration 5:   log likelihood = -93.759919  
Iteration 6:   log likelihood = -93.757821  
Iteration 7:   log likelihood = -93.757352  
Iteration 8:   log likelihood = -93.757236  
Iteration 9:   log likelihood = -93.757213  
Iteration 10:  log likelihood = -93.757208  

Multinomial logistic regression                 Number of obs     =        118
                                                LR chi2(12)       =      19.00
                                                Prob > chi2       =     0.0885
Log likelihood = -93.757208                     Pseudo R2         =     0.0920

------------------------------------------------------------------------------
disapptime~4 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0            |
 1.posevents |  -1.976839   1.295792    -1.53   0.127    -4.516544    .5628668
 1.negevents |   -2.10056   1.304011    -1.61   0.107    -4.656375    .4552546
   1.posbush |   15.75549   873.1916     0.02   0.986    -1695.669     1727.18
   1.negbush |   .9424272   1.102402     0.85   0.393    -1.218242    3.103096
       _cons |   .3146791   .9440761     0.33   0.739    -1.535676    2.165034
-------------+----------------------------------------------------------------
1            |  (base outcome)
-------------+----------------------------------------------------------------
2            |
 1.posevents |  -2.247635    1.19049    -1.89   0.059    -4.580952    .0856823
 1.negevents |  -1.780507   1.132358    -1.57   0.116    -3.999888    .4388744
   1.posbush |   16.50042   873.1916     0.02   0.985    -1694.924    1727.924
   1.negbush |   2.444964   1.072278     2.28   0.023     .3433378    4.546591
       _cons |  -.1077583    1.02057    -0.11   0.916    -2.108039    1.892523
-------------+----------------------------------------------------------------
3            |
 1.posevents |  -.8937606   .9411479    -0.95   0.342    -2.738377    .9508554
 1.negevents |  -.9214935    .934176    -0.99   0.324    -2.752445    .9094577
   1.posbush |   15.14715   873.1911     0.02   0.986    -1696.276     1726.57
   1.negbush |   .2562956   .7179463     0.36   0.721    -1.150853    1.663444
       _cons |   2.391246   .7498763     3.19   0.001     .9215159    3.860977
------------------------------------------------------------------------------

. margins r.posevents, at(negevents=0 posbush=0 negbush=0);

Contrasts of adjusted predictions
Model VCE    : OIM

1._predict   : Pr(disapptimetable4==0), predict(pr outcome(0))
2._predict   : Pr(disapptimetable4==1), predict(pr outcome(1))
3._predict   : Pr(disapptimetable4==2), predict(pr outcome(2))
4._predict   : Pr(disapptimetable4==3), predict(pr outcome(3))
at           : negevents       =           0
               posbush         =           0
               negbush         =           0

------------------------------------------------------
                   |         df        chi2     P>chi2
-------------------+----------------------------------
posevents@_predict |
       (1 vs 0) 1  |          1        1.42     0.2341
       (1 vs 0) 2  |          1        1.06     0.3036
       (1 vs 0) 3  |          1        1.59     0.2080
       (1 vs 0) 4  |          1        0.00     0.9505
            Joint  |          3        3.89     0.2741
------------------------------------------------------

--------------------------------------------------------------------
                   |            Delta-method
                   |   Contrast   Std. Err.     [95% Conf. Interval]
-------------------+------------------------------------------------
posevents@_predict |
       (1 vs 0) 1  |  -.0635342   .0533942      -.168185    .0411166
       (1 vs 0) 2  |   .1033128   .1004279     -.0935223    .3001479
       (1 vs 0) 3  |  -.0467694    .037142     -.1195665    .0260276
       (1 vs 0) 4  |   .0069908   .1126464     -.2137921    .2277736
--------------------------------------------------------------------

. matrix pe31=r(table)';

. matrix pe3=pe31[1..4,1..2];

. margins r.negbush, at(posevents=0 negevents=0 posbush=0);

Contrasts of adjusted predictions
Model VCE    : OIM

1._predict   : Pr(disapptimetable4==0), predict(pr outcome(0))
2._predict   : Pr(disapptimetable4==1), predict(pr outcome(1))
3._predict   : Pr(disapptimetable4==2), predict(pr outcome(2))
4._predict   : Pr(disapptimetable4==3), predict(pr outcome(3))
at           : posevents       =           0
               negevents       =           0
               posbush         =           0

----------------------------------------------------
                 |         df        chi2     P>chi2
-----------------+----------------------------------
negbush@_predict |
     (1 vs 0) 1  |          1        0.08     0.7803
     (1 vs 0) 2  |          1        0.93     0.3358
     (1 vs 0) 3  |          1        5.22     0.0224
     (1 vs 0) 4  |          1        4.76     0.0292
          Joint  |          3        6.46     0.0914
----------------------------------------------------

------------------------------------------------------------------
                 |            Delta-method
                 |   Contrast   Std. Err.     [95% Conf. Interval]
-----------------+------------------------------------------------
negbush@_predict |
     (1 vs 0) 1  |   .0247685   .0888102     -.1492963    .1988332
     (1 vs 0) 2  |    -.03595   .0373495     -.1091536    .0372536
     (1 vs 0) 3  |   .2938857   .1286624      .0417121    .5460593
     (1 vs 0) 4  |  -.2827042    .129624     -.5367625   -.0286459
------------------------------------------------------------------

. matrix nb31=r(table)';

. matrix nb3=nb31[1..4,1..2];

. matrix ob31=r(_N);

. matrix ob3=ob31[1,1..2];

. log close;
      name:  <unnamed>
       log:  /home/ppaolino/research/projects/mlogit/polan/mlogit_replication.o
> ut
  log type:  text
 closed on:  10 Jul 2020, 11:20:48
-------------------------------------------------------------------------------
-------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /home/ppaolino/research/projects/mlogit/polan/mlogit_replication.o
> ut
  log type:  text
 opened on:  10 Jul 2020, 11:20:48

. /* Reproduce results from Table A.2 in the Supplemental Materials */
> 
> mlogit iraqwin i.posevents i.negevents i.posbush i.negbush if
> bushapp4==0, base(1);

Iteration 0:   log likelihood = -454.90947  
Iteration 1:   log likelihood = -445.78314  
Iteration 2:   log likelihood = -445.08987  
Iteration 3:   log likelihood = -444.94996  
Iteration 4:   log likelihood = -444.91915  
Iteration 5:   log likelihood =   -444.913  
Iteration 6:   log likelihood =  -444.9116  
Iteration 7:   log likelihood = -444.91124  
Iteration 8:   log likelihood = -444.91118  
Iteration 9:   log likelihood = -444.91116  

Multinomial logistic regression                 Number of obs     =        449
                                                LR chi2(12)       =      20.00
                                                Prob > chi2       =     0.0672
Log likelihood = -444.91116                     Pseudo R2         =     0.0220

------------------------------------------------------------------------------
     iraqwin |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0            |
 1.posevents |      .1674    .260203     0.64   0.520    -.3425885    .6773884
 1.negevents |   .4114368   .2522565     1.63   0.103    -.0829768    .9058504
   1.posbush |  -.2355735    .249375    -0.94   0.345    -.7243395    .2531925
   1.negbush |  -.5118431   .2603602    -1.97   0.049     -1.02214   -.0015465
       _cons |  -.3781185   .2273226    -1.66   0.096    -.8236626    .0674255
-------------+----------------------------------------------------------------
1            |  (base outcome)
-------------+----------------------------------------------------------------
2            |
 1.posevents |   .9943443   .4117164     2.42   0.016     .1873951    1.801294
 1.negevents |   .9822414   .4140425     2.37   0.018      .170733     1.79375
   1.posbush |  -.5581874   .3814214    -1.46   0.143     -1.30576    .1893848
   1.negbush |  -.3153649   .3563749    -0.88   0.376    -1.013847    .3831169
       _cons |  -1.896209   .3831595    -4.95   0.000    -2.647187    -1.14523
-------------+----------------------------------------------------------------
3            |
 1.posevents |   .1521965    1.01873     0.15   0.881    -1.844477     2.14887
 1.negevents |  -.4721491    1.23805    -0.38   0.703    -2.898682    1.954384
   1.posbush |   13.67043   571.3053     0.02   0.981    -1106.067    1133.408
   1.negbush |   13.23803   571.3054     0.02   0.982      -1106.5    1132.976
       _cons |  -16.89801   571.3052    -0.03   0.976    -1136.636     1102.84
------------------------------------------------------------------------------

. margins r.posevents, at(negevents=0 posbush=0 negbush=0);

Contrasts of adjusted predictions
Model VCE    : OIM

1._predict   : Pr(iraqwin==0), predict(pr outcome(0))
2._predict   : Pr(iraqwin==1), predict(pr outcome(1))
3._predict   : Pr(iraqwin==2), predict(pr outcome(2))
4._predict   : Pr(iraqwin==3), predict(pr outcome(3))
at           : negevents       =           0
               posbush         =           0
               negbush         =           0

------------------------------------------------------
                   |         df        chi2     P>chi2
-------------------+----------------------------------
posevents@_predict |
       (1 vs 0) 1  |          1        0.02     0.8954
       (1 vs 0) 2  |          1        2.45     0.1172
       (1 vs 0) 3  |          1        5.12     0.0236
       (1 vs 0) 4  |          1        0.00     0.9986
            Joint  |          3        5.67     0.1286
------------------------------------------------------

--------------------------------------------------------------------
                   |            Delta-method
                   |   Contrast   Std. Err.     [95% Conf. Interval]
-------------------+------------------------------------------------
posevents@_predict |
       (1 vs 0) 1  |  -.0077652   .0590798     -.1235595    .1080291
       (1 vs 0) 2  |  -.0935728   .0597324     -.2106462    .0235005
       (1 vs 0) 3  |    .101338   .0447835      .0135639    .1891122
       (1 vs 0) 4  |  -8.89e-10   5.08e-07     -9.97e-07    9.95e-07
--------------------------------------------------------------------

. matrix pe01=r(table)';

. matrix pe0=pe01[1..4,1..2];

. matrix ob01=r(_N);

. matrix ob0=ob01[1,1..2];

. mlogit iraqwin i.posevents i.negevents i.posbush i.negbush if
> bushapp4==1, base(1);

Iteration 0:   log likelihood = -165.78672  
Iteration 1:   log likelihood = -155.60305  
Iteration 2:   log likelihood = -155.22639  
Iteration 3:   log likelihood = -155.22551  
Iteration 4:   log likelihood = -155.22551  

Multinomial logistic regression                 Number of obs     =        145
                                                LR chi2(12)       =      21.12
                                                Prob > chi2       =     0.0486
Log likelihood = -155.22551                     Pseudo R2         =     0.0637

------------------------------------------------------------------------------
     iraqwin |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0            |
 1.posevents |   .6706117   .9048504     0.74   0.459    -1.102862    2.444086
 1.negevents |   .7568045   .7744064     0.98   0.328    -.7610042    2.274613
   1.posbush |  -.1188703   .9694032    -0.12   0.902    -2.018866    1.781125
   1.negbush |   .7031853     .76751     0.92   0.360    -.8011067    2.207477
       _cons |  -2.532482   .7455136    -3.40   0.001    -3.993662   -1.071302
-------------+----------------------------------------------------------------
1            |  (base outcome)
-------------+----------------------------------------------------------------
2            |
 1.posevents |   1.096527   .4804434     2.28   0.022     .1548752    2.038179
 1.negevents |  -.1387953    .471431    -0.29   0.768    -1.062783    .7851925
   1.posbush |   .5605967   .4651196     1.21   0.228    -.3510211    1.472214
   1.negbush |  -.2847201   .4890024    -0.58   0.560    -1.243147    .6737069
       _cons |  -.6766984   .3742102    -1.81   0.071    -1.410137    .0567401
-------------+----------------------------------------------------------------
3            |
 1.posevents |   1.796784   .7916696     2.27   0.023     .2451403    3.348428
 1.negevents |    .269209    .875461     0.31   0.758    -1.446663    1.985081
   1.posbush |   1.058474   .7754528     1.36   0.172    -.4613858    2.578333
   1.negbush |   -.315397   .8969905    -0.35   0.725    -2.073466    1.442672
       _cons |  -2.701014   .7704736    -3.51   0.000    -4.211115   -1.190914
------------------------------------------------------------------------------

. margins r.posevents, at(negevents=0 posbush=0 negbush=0);

Contrasts of adjusted predictions
Model VCE    : OIM

1._predict   : Pr(iraqwin==0), predict(pr outcome(0))
2._predict   : Pr(iraqwin==1), predict(pr outcome(1))
3._predict   : Pr(iraqwin==2), predict(pr outcome(2))
4._predict   : Pr(iraqwin==3), predict(pr outcome(3))
at           : negevents       =           0
               posbush         =           0
               negbush         =           0

------------------------------------------------------
                   |         df        chi2     P>chi2
-------------------+----------------------------------
posevents@_predict |
       (1 vs 0) 1  |          1        0.00     0.9536
       (1 vs 0) 2  |          1        7.61     0.0058
       (1 vs 0) 3  |          1        3.06     0.0802
       (1 vs 0) 4  |          1        1.64     0.2007
            Joint  |          3        8.00     0.0460
------------------------------------------------------

--------------------------------------------------------------------
                   |            Delta-method
                   |   Contrast   Std. Err.     [95% Conf. Interval]
-------------------+------------------------------------------------
posevents@_predict |
       (1 vs 0) 1  |   .0024008   .0412862     -.0785187    .0833203
       (1 vs 0) 2  |  -.2797979   .1014347     -.4786063   -.0809895
       (1 vs 0) 3  |   .1866028   .1066557     -.0224385    .3956441
       (1 vs 0) 4  |   .0907943   .0709605     -.0482858    .2298744
--------------------------------------------------------------------

. matrix pe11=r(table)';

. matrix pe1=pe11[1..4,1..2];

. matrix ob11=r(_N);

. matrix ob1=ob11[1,1..2];

. mlogit iraqwin i.posevents i.negevents i.posbush i.negbush if
> bushapp4==2, base(1);

Iteration 0:   log likelihood = -273.82259  
Iteration 1:   log likelihood = -270.22627  
Iteration 2:   log likelihood = -267.60279  
Iteration 3:   log likelihood = -267.40703  
Iteration 4:   log likelihood = -267.35141  
Iteration 5:   log likelihood = -267.34169  
Iteration 6:   log likelihood =  -267.3394  
Iteration 7:   log likelihood = -267.33885  
Iteration 8:   log likelihood = -267.33873  
Iteration 9:   log likelihood = -267.33871  

Multinomial logistic regression                 Number of obs     =        284
                                                LR chi2(12)       =      12.97
                                                Prob > chi2       =     0.3714
Log likelihood = -267.33871                     Pseudo R2         =     0.0237

------------------------------------------------------------------------------
     iraqwin |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0            |
 1.posevents |   .1929636   1160.107     0.00   1.000    -2273.574     2273.96
 1.negevents |   13.90387    871.154     0.02   0.987    -1693.527    1721.334
   1.posbush |  -13.08024   549.7203    -0.02   0.981    -1090.512    1064.352
   1.negbush |  -.5100008   1.497591    -0.34   0.733    -3.445226    2.425224
       _cons |  -15.02104   871.1539    -0.02   0.986    -1722.451    1692.409
-------------+----------------------------------------------------------------
1            |  (base outcome)
-------------+----------------------------------------------------------------
2            |
 1.posevents |   .2818274   .4992812     0.56   0.572    -.6967457    1.260401
 1.negevents |   -.294183    .481522    -0.61   0.541    -1.237949    .6495827
   1.posbush |  -.5281129   .5008956    -1.05   0.292     -1.50985    .4536245
   1.negbush |  -.1947484   .4952231    -0.39   0.694    -1.165368    .7758711
       _cons |   1.872406   .4803123     3.90   0.000     .9310114    2.813801
-------------+----------------------------------------------------------------
3            |
 1.posevents |   .0256342   .5264226     0.05   0.961    -1.006135    1.057404
 1.negevents |  -.5120225   .5137441    -1.00   0.319    -1.518942    .4948974
   1.posbush |   -.554833   .5233867    -1.06   0.289    -1.580652    .4709862
   1.negbush |  -.6936151   .5290982    -1.31   0.190    -1.730629    .3433984
       _cons |   1.604057   .4973729     3.23   0.001      .629224     2.57889
------------------------------------------------------------------------------

. margins r.posevents, at(negevents=0 posbush=0 negbush=0);

Contrasts of adjusted predictions
Model VCE    : OIM

1._predict   : Pr(iraqwin==0), predict(pr outcome(0))
2._predict   : Pr(iraqwin==1), predict(pr outcome(1))
3._predict   : Pr(iraqwin==2), predict(pr outcome(2))
4._predict   : Pr(iraqwin==3), predict(pr outcome(3))
at           : negevents       =           0
               posbush         =           0
               negbush         =           0

------------------------------------------------------
                   |         df        chi2     P>chi2
-------------------+----------------------------------
posevents@_predict |
       (1 vs 0) 1  |          1        0.00     1.0000
       (1 vs 0) 2  |          1        0.13     0.7156
       (1 vs 0) 3  |          1        0.78     0.3785
       (1 vs 0) 4  |          1        0.52     0.4728
            Joint  |          3        0.80     0.8497
------------------------------------------------------

--------------------------------------------------------------------
                   |            Delta-method
                   |   Contrast   Std. Err.     [95% Conf. Interval]
-------------------+------------------------------------------------
posevents@_predict |
       (1 vs 0) 1  |   6.67e-10   .0000282     -.0000553    .0000553
       (1 vs 0) 2  |  -.0122286   .0335592     -.0780034    .0535462
       (1 vs 0) 3  |   .0642731   .0729856      -.078776    .2073221
       (1 vs 0) 4  |  -.0520445   .0724974     -.1941367    .0900477
--------------------------------------------------------------------

. matrix pe21=r(table)';

. matrix pe2=pe21[1..4,1..2];

. matrix ob21=r(_N);

. matrix ob2=ob21[1,1..2];

. mlogit iraqwin i.posevents i.negevents i.posbush i.negbush if
> bushapp4==3, base(1);

Iteration 0:   log likelihood = -88.595916  
Iteration 1:   log likelihood = -83.607653  
Iteration 2:   log likelihood = -81.578277  
Iteration 3:   log likelihood = -81.359043  
Iteration 4:   log likelihood = -81.307833  
Iteration 5:   log likelihood = -81.296287  
Iteration 6:   log likelihood =   -81.2936  
Iteration 7:   log likelihood = -81.292969  
Iteration 8:   log likelihood = -81.292836  
Iteration 9:   log likelihood = -81.292814  
Iteration 10:  log likelihood = -81.292811  

Multinomial logistic regression                 Number of obs     =        118
                                                LR chi2(12)       =      14.61
                                                Prob > chi2       =     0.2637
Log likelihood = -81.292811                     Pseudo R2         =     0.0824

------------------------------------------------------------------------------
     iraqwin |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
0            |
 1.posevents |  -15.72651   3839.373    -0.00   0.997     -7540.76    7509.306
 1.negevents |    -16.464   3611.992    -0.00   0.996    -7095.839    7062.911
   1.posbush |   .9659734   4851.669     0.00   1.000    -9508.131    9510.063
   1.negbush |  -16.54095   3146.423    -0.01   0.996    -6183.416    6150.334
       _cons |    .933157   1.596256     0.58   0.559    -2.195446     4.06176
-------------+----------------------------------------------------------------
1            |  (base outcome)
-------------+----------------------------------------------------------------
2            |
 1.posevents |  -.5852467     1.5186    -0.39   0.700    -3.561649    2.391156
 1.negevents |   -1.16026    1.32666    -0.87   0.382    -3.760466    1.439947
   1.posbush |   15.15796    2888.33     0.01   0.996    -5645.865    5676.181
   1.negbush |  -.9081831   1.248329    -0.73   0.467    -3.354864    1.538498
       _cons |   3.031019   1.272893     2.38   0.017     .5361951    5.525843
-------------+----------------------------------------------------------------
3            |
 1.posevents |   .4567206   1.482063     0.31   0.758     -2.44807    3.361512
 1.negevents |  -.3957974   1.294713    -0.31   0.760    -2.933388    2.141794
   1.posbush |   15.70046    2888.33     0.01   0.996    -5645.322    5676.723
   1.negbush |  -1.359009   1.210295    -1.12   0.261    -3.731143    1.013125
       _cons |   3.532124   1.256402     2.81   0.005     1.069621    5.994628
------------------------------------------------------------------------------

. margins r.posevents, at(negevents=0 posbush=0 negbush=0);

Contrasts of adjusted predictions
Model VCE    : OIM

1._predict   : Pr(iraqwin==0), predict(pr outcome(0))
2._predict   : Pr(iraqwin==1), predict(pr outcome(1))
3._predict   : Pr(iraqwin==2), predict(pr outcome(2))
4._predict   : Pr(iraqwin==3), predict(pr outcome(3))
at           : negevents       =           0
               posbush         =           0
               negbush         =           0

------------------------------------------------------
                   |         df        chi2     P>chi2
-------------------+----------------------------------
posevents@_predict |
       (1 vs 0) 1  |          1        1.05     0.3065
       (1 vs 0) 2  |          1        0.01     0.9283
       (1 vs 0) 3  |          1        3.31     0.0690
       (1 vs 0) 4  |          1        4.80     0.0284
            Joint  |          3        5.11     0.1640
------------------------------------------------------

--------------------------------------------------------------------
                   |            Delta-method
                   |   Contrast   Std. Err.     [95% Conf. Interval]
-------------------+------------------------------------------------
posevents@_predict |
       (1 vs 0) 1  |  -.0434936   .0425297     -.1268504    .0398632
       (1 vs 0) 2  |  -.0020761   .0230686     -.0472896    .0431374
       (1 vs 0) 3  |  -.1809754   .0995236      -.376038    .0140873
       (1 vs 0) 4  |   .2265451   .1033967      .0238913    .4291988
--------------------------------------------------------------------

. matrix pe31=r(table)';

. matrix pe3=pe31[1..4,1..2];

. matrix ob31=r(_N);

. matrix ob3=ob31[1,1..2];

. log close;
      name:  <unnamed>
       log:  /home/ppaolino/research/projects/mlogit/polan/mlogit_replication.o
> ut
  log type:  text
 closed on:  10 Jul 2020, 11:20:48
-------------------------------------------------------------------------------
-------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /home/ppaolino/research/projects/mlogit/polan/mlogit_replication.o
> ut
  log type:  text
 opened on:  10 Jul 2020, 11:20:48

. /* Reproduce results for Greenhill and Oppenheim (2017) */
> /* Thailand coup rumors first */
> 
> clear;

. use "2015-05-0216.R2 Rumor_Has_It--_Thailand_replication_data.dta";

. /* copy multinomial logit code from archived do file */
> 
> /* Greenhill and Oppenheim Table 4 model (4): Full sample */
> mlogit believe_coup distrust_military threat_perception
> heard_coup_rumor male age muslim income education social_trust
> local_participation, base(0) r rrr;

Iteration 0:   log pseudolikelihood =  -1074.209  
Iteration 1:   log pseudolikelihood = -999.99652  
Iteration 2:   log pseudolikelihood = -964.72423  
Iteration 3:   log pseudolikelihood = -964.01338  
Iteration 4:   log pseudolikelihood = -964.00955  
Iteration 5:   log pseudolikelihood = -964.00955  

Multinomial logistic regression                 Number of obs     =      1,298
                                                Wald chi2(20)     =     185.73
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -964.00955               Pseudo R2         =     0.1026

------------------------------------------------------------------------------
             |               Robust
believe_coup |        RRR   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
No           |  (base outcome)
-------------+----------------------------------------------------------------
Maybe        |
distrust_m~y |   1.228719   .0842238     3.00   0.003     1.074251    1.405397
threat_per~n |   1.132393   .0500934     2.81   0.005     1.038348    1.234956
heard_coup~r |   3.809525   .6602419     7.72   0.000     2.712352    5.350514
        male |   .9520586   .1218061    -0.38   0.701     .7409027    1.223393
         age |   .9901705    .005048    -1.94   0.053     .9803258    1.000114
      muslim |   .6773632   .0997446    -2.65   0.008     .5075496    .9039921
      income |   .9526319    .070143    -0.66   0.510     .8246137    1.100525
   education |   .9046084   .0535166    -1.69   0.090     .8055705    1.015822
social_trust |   .8088046   .1062082    -1.62   0.106     .6252706    1.046211
local_part~n |   1.277486   .1672733     1.87   0.061     .9883266    1.651247
       _cons |   1.746761   .7596151     1.28   0.200     .7448537    4.096341
-------------+----------------------------------------------------------------
Yes          |
distrust_m~y |   1.427898   .2601896     1.95   0.051     .9990618    2.040807
threat_per~n |   1.357273   .1367891     3.03   0.002      1.11399    1.653687
heard_coup~r |   38.76253   14.34029     9.89   0.000      18.7719    80.04165
        male |   1.567193   .4513218     1.56   0.119     .8912361     2.75583
         age |   .9740767   .0115658    -2.21   0.027     .9516699    .9970111
      muslim |   .6587366   .2205907    -1.25   0.213     .3417201    1.269852
      income |    1.13369   .2129555     0.67   0.504     .7845168    1.638274
   education |   .7184771   .0929363    -2.56   0.011     .5575816    .9258004
social_trust |   1.086611   .3208015     0.28   0.778     .6092182    1.938097
local_part~n |   2.283068   .6692023     2.82   0.005     1.285342    4.055264
       _cons |   .0193978   .0224736    -3.40   0.001     .0020025    .1878996
------------------------------------------------------------------------------
Note: _cons estimates baseline relative risk for each outcome.

. /* threat perception: significance levels of marginal effects smaller
> than the levels for the MNL coefficients */
> margins, dydx(threat_perception);

Average marginal effects                        Number of obs     =      1,298
Model VCE    : Robust

dy/dx w.r.t. : threat_perception
1._predict   : Pr(believe_coup==No), predict(pr outcome(0))
2._predict   : Pr(believe_coup==Maybe), predict(pr outcome(1))
3._predict   : Pr(believe_coup==Yes), predict(pr outcome(2))

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
threat_per~n |
    _predict |
          1  |   -.027247   .0089429    -3.05   0.002    -.0447747   -.0097192
          2  |   .0183843   .0094724     1.94   0.052    -.0001812    .0369498
          3  |   .0088627    .004042     2.19   0.028     .0009405    .0167848
------------------------------------------------------------------------------

. margins, at((mean) _all threat_perception=(1(1)4)) post;

Adjusted predictions                            Number of obs     =      1,298
Model VCE    : Robust

1._predict   : Pr(believe_coup==No), predict(pr outcome(0))
2._predict   : Pr(believe_coup==Maybe), predict(pr outcome(1))
3._predict   : Pr(believe_coup==Yes), predict(pr outcome(2))

1._at        : distrust_m~y    =    2.826656 (mean)
               threat_per~n    =           1
               heard_coup~r    =    .2604006 (mean)
               male            =     .422188 (mean)
               age             =    42.01233 (mean)
               muslim          =    .6725732 (mean)
               income          =    2.840524 (mean)
               education       =    2.304314 (mean)
               social_trust    =    .3382126 (mean)
               local_part~n    =    .4206471 (mean)

2._at        : distrust_m~y    =    2.826656 (mean)
               threat_per~n    =           2
               heard_coup~r    =    .2604006 (mean)
               male            =     .422188 (mean)
               age             =    42.01233 (mean)
               muslim          =    .6725732 (mean)
               income          =    2.840524 (mean)
               education       =    2.304314 (mean)
               social_trust    =    .3382126 (mean)
               local_part~n    =    .4206471 (mean)

3._at        : distrust_m~y    =    2.826656 (mean)
               threat_per~n    =           3
               heard_coup~r    =    .2604006 (mean)
               male            =     .422188 (mean)
               age             =    42.01233 (mean)
               muslim          =    .6725732 (mean)
               income          =    2.840524 (mean)
               education       =    2.304314 (mean)
               social_trust    =    .3382126 (mean)
               local_part~n    =    .4206471 (mean)

4._at        : distrust_m~y    =    2.826656 (mean)
               threat_per~n    =           4
               heard_coup~r    =    .2604006 (mean)
               male            =     .422188 (mean)
               age             =    42.01233 (mean)
               muslim          =    .6725732 (mean)
               income          =    2.840524 (mean)
               education       =    2.304314 (mean)
               social_trust    =    .3382126 (mean)
               local_part~n    =    .4206471 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_predict#_at |
        1 1  |   .3526086   .0155019    22.75   0.000     .3222255    .3829917
        1 2  |   .3234756   .0149788    21.60   0.000     .2941177    .3528335
        1 3  |   .2954152   .0193408    15.27   0.000     .2575079    .3333225
        1 4  |   .2685594   .0252013    10.66   0.000     .2191657    .3179532
        2 1  |   .6280376   .0156074    40.24   0.000     .5974476    .6586276
        2 2  |   .6524264   .0152413    42.81   0.000      .622554    .6822987
        2 3  |   .6747145   .0197117    34.23   0.000     .6360803    .7133486
        2 4  |    .694584   .0259108    26.81   0.000     .6437997    .7453683
        3 1  |   .0193538    .004571     4.23   0.000     .0103947    .0283128
        3 2  |    .024098   .0051878     4.65   0.000     .0139301    .0342659
        3 3  |   .0298703   .0069273     4.31   0.000      .016293    .0434476
        3 4  |   .0368565   .0102515     3.60   0.000      .016764    .0569491
------------------------------------------------------------------------------

. /* Reproduce Figure 1a */
> 
> marginsplot, xdimension(threat_perception) title(Acceptance of Coup
> Rumors) ytitle(Predicted Probability) plot1opts(lpattern(solid)
> msymbol(O)) plot2opts(lpattern(dash) msymbol(T))
> plot3opts(lpattern(dash) msymbol(Dh)) xlabel(1 "Very Unlikely" 4 "Very
> Likely", labsize(vsmall)) plotd(,label( "Deny" "Plausible" "Accept"))
> legend(row(1));

  Variables that uniquely identify margins: threat_perception _outcome

. graph export figures/figure1a.eps, as(eps) replace;
(note: file figures/figure1a.eps not found)
(file figures/figure1a.eps written in EPS format)

. /* Delete next line if not using linux */
> !epstopdf /figures/figure1a.eps -o=/figures/figure1a.pdf;


. quietly: mlogit believe_coup distrust_military threat_perception
> heard_coup_rumor male age muslim income education social_trust
> local_participation, base(0) r rrr;

. margins, at((mean) _all threat_perception=(1(1)4)) post;

Adjusted predictions                            Number of obs     =      1,298
Model VCE    : Robust

1._predict   : Pr(believe_coup==No), predict(pr outcome(0))
2._predict   : Pr(believe_coup==Maybe), predict(pr outcome(1))
3._predict   : Pr(believe_coup==Yes), predict(pr outcome(2))

1._at        : distrust_m~y    =    2.826656 (mean)
               threat_per~n    =           1
               heard_coup~r    =    .2604006 (mean)
               male            =     .422188 (mean)
               age             =    42.01233 (mean)
               muslim          =    .6725732 (mean)
               income          =    2.840524 (mean)
               education       =    2.304314 (mean)
               social_trust    =    .3382126 (mean)
               local_part~n    =    .4206471 (mean)

2._at        : distrust_m~y    =    2.826656 (mean)
               threat_per~n    =           2
               heard_coup~r    =    .2604006 (mean)
               male            =     .422188 (mean)
               age             =    42.01233 (mean)
               muslim          =    .6725732 (mean)
               income          =    2.840524 (mean)
               education       =    2.304314 (mean)
               social_trust    =    .3382126 (mean)
               local_part~n    =    .4206471 (mean)

3._at        : distrust_m~y    =    2.826656 (mean)
               threat_per~n    =           3
               heard_coup~r    =    .2604006 (mean)
               male            =     .422188 (mean)
               age             =    42.01233 (mean)
               muslim          =    .6725732 (mean)
               income          =    2.840524 (mean)
               education       =    2.304314 (mean)
               social_trust    =    .3382126 (mean)
               local_part~n    =    .4206471 (mean)

4._at        : distrust_m~y    =    2.826656 (mean)
               threat_per~n    =           4
               heard_coup~r    =    .2604006 (mean)
               male            =     .422188 (mean)
               age             =    42.01233 (mean)
               muslim          =    .6725732 (mean)
               income          =    2.840524 (mean)
               education       =    2.304314 (mean)
               social_trust    =    .3382126 (mean)
               local_part~n    =    .4206471 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_predict#_at |
        1 1  |   .3526086   .0155019    22.75   0.000     .3222255    .3829917
        1 2  |   .3234756   .0149788    21.60   0.000     .2941177    .3528335
        1 3  |   .2954152   .0193408    15.27   0.000     .2575079    .3333225
        1 4  |   .2685594   .0252013    10.66   0.000     .2191657    .3179532
        2 1  |   .6280376   .0156074    40.24   0.000     .5974476    .6586276
        2 2  |   .6524264   .0152413    42.81   0.000      .622554    .6822987
        2 3  |   .6747145   .0197117    34.23   0.000     .6360803    .7133486
        2 4  |    .694584   .0259108    26.81   0.000     .6437997    .7453683
        3 1  |   .0193538    .004571     4.23   0.000     .0103947    .0283128
        3 2  |    .024098   .0051878     4.65   0.000     .0139301    .0342659
        3 3  |   .0298703   .0069273     4.31   0.000      .016293    .0434476
        3 4  |   .0368565   .0102515     3.60   0.000      .016764    .0569491
------------------------------------------------------------------------------

. /* predicted probability difference between low and high threat
> perception */
> lincom _b[3._predict#1bn._at] - _b[3._predict#4._at];

 ( 1)  3._predict#1bn._at - 3._predict#4._at = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0175028   .0092019    -1.90   0.057    -.0355382    .0005327
------------------------------------------------------------------------------

. /* Now Philippines corruption rumors */
> 
> clear;

. use "2015-05-0216.R2 Rumor_Has_It--_Philippines_replication_data.dta";

. /* Add in values for dependent variable */
> 
> label define believe_IRA_v 0 "Deny" 1 "Plausible" 2 "Accept";

. label values believe_IRA believe_IRA_v;

. /* Greenhill and Oppenheim Table 7 MNL model (3): Unexposed sample */
> 
> mlogit believe_IRA distrust_local_officials economic_fear male age
> muslim income education social_trust  local_participation if
> heard_IRA==0, base(0) r rrr;

Iteration 0:   log pseudolikelihood = -733.34849  
Iteration 1:   log pseudolikelihood = -594.27863  
Iteration 2:   log pseudolikelihood = -592.37815  
Iteration 3:   log pseudolikelihood = -592.37109  
Iteration 4:   log pseudolikelihood = -592.37109  

Multinomial logistic regression                 Number of obs     =        735
                                                Wald chi2(18)     =     207.79
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -592.37109               Pseudo R2         =     0.1922

------------------------------------------------------------------------------
             |               Robust
 believe_IRA |        RRR   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
Deny         |  (base outcome)
-------------+----------------------------------------------------------------
Plausible    |
distrust_l~s |   1.525409   .1614009     3.99   0.000     1.239716     1.87694
economic_f~r |   1.719869   .8235601     1.13   0.257     .6728174    4.396364
        male |    .964321   .1920297    -0.18   0.855     .6527065    1.424706
         age |   1.007334   .0073418     1.00   0.316     .9930467    1.021827
      muslim |   .0922544   .0203154   -10.82   0.000     .0599161    .1420466
      income |    1.08151   .1385255     0.61   0.541     .8414032    1.390135
   education |   1.285534   .1258119     2.57   0.010     1.061155    1.557358
social_trust |   .6880625   .0849302    -3.03   0.002     .5402078    .8763851
local_part~n |   1.047848   .2398975     0.20   0.838     .6689935    1.641251
       _cons |   1.295587   1.434262     0.23   0.815     .1479636    11.34431
-------------+----------------------------------------------------------------
Accept       |
distrust_l~s |   1.358967   .1670284     2.50   0.013     1.068044    1.729134
economic_f~r |   1.922297   .8681081     1.45   0.148     .7932652    4.658248
        male |   1.041623   .2412553     0.18   0.860     .6615458    1.640067
         age |   1.000972   .0082424     0.12   0.906     .9849471    1.017258
      muslim |   .6479023   .1779368    -1.58   0.114     .3782157    1.109889
      income |    1.18264   .1644912     1.21   0.228     .9004523    1.553262
   education |   1.152776   .1145679     1.43   0.153     .9487436    1.400687
social_trust |   .7933678   .1176561    -1.56   0.119     .5932555     1.06098
local_part~n |   2.774898   .8870213     3.19   0.001     1.483039    5.192079
       _cons |   .0196736   .0269722    -2.87   0.004     .0013394    .2889807
------------------------------------------------------------------------------
Note: _cons estimates baseline relative risk for each outcome.

. margins, at((mean) _all distrust_local_officials=(1(1)5)) post;

Adjusted predictions                            Number of obs     =        735
Model VCE    : Robust

1._predict   : Pr(believe_IRA==Deny), predict(pr outcome(0))
2._predict   : Pr(believe_IRA==Plausible), predict(pr outcome(1))
3._predict   : Pr(believe_IRA==Accept), predict(pr outcome(2))

1._at        : distrust_l~s    =           1
               economic_f~r    =    .0653061 (mean)
               male            =    .4761905 (mean)
               age             =    40.46259 (mean)
               muslim          =    .4993197 (mean)
               income          =    7.844926 (mean)
               education       =    2.363265 (mean)
               social_trust    =    2.176871 (mean)
               local_part~n    =    1.741497 (mean)

2._at        : distrust_l~s    =           2
               economic_f~r    =    .0653061 (mean)
               male            =    .4761905 (mean)
               age             =    40.46259 (mean)
               muslim          =    .4993197 (mean)
               income          =    7.844926 (mean)
               education       =    2.363265 (mean)
               social_trust    =    2.176871 (mean)
               local_part~n    =    1.741497 (mean)

3._at        : distrust_l~s    =           3
               economic_f~r    =    .0653061 (mean)
               male            =    .4761905 (mean)
               age             =    40.46259 (mean)
               muslim          =    .4993197 (mean)
               income          =    7.844926 (mean)
               education       =    2.363265 (mean)
               social_trust    =    2.176871 (mean)
               local_part~n    =    1.741497 (mean)

4._at        : distrust_l~s    =           4
               economic_f~r    =    .0653061 (mean)
               male            =    .4761905 (mean)
               age             =    40.46259 (mean)
               muslim          =    .4993197 (mean)
               income          =    7.844926 (mean)
               education       =    2.363265 (mean)
               social_trust    =    2.176871 (mean)
               local_part~n    =    1.741497 (mean)

5._at        : distrust_l~s    =           5
               economic_f~r    =    .0653061 (mean)
               male            =    .4761905 (mean)
               age             =    40.46259 (mean)
               muslim          =    .4993197 (mean)
               income          =    7.844926 (mean)
               education       =    2.363265 (mean)
               social_trust    =    2.176871 (mean)
               local_part~n    =    1.741497 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_predict#_at |
        1 1  |   .3612165   .0276599    13.06   0.000     .3070041    .4154288
        1 2  |   .2759656   .0217528    12.69   0.000      .233331    .3186002
        1 3  |   .2040126   .0280571     7.27   0.000     .1490217    .2590035
        1 4  |   .1467331   .0327932     4.47   0.000     .0824597    .2110065
        1 5  |   .1032539   .0331775     3.11   0.002     .0382271    .1682807
        2 1  |   .4783432   .0286942    16.67   0.000     .4221036    .5345829
        2 2  |   .5574595   .0229676    24.27   0.000     .5124437    .6024752
        2 3  |   .6286394   .0310754    20.23   0.000     .5677329     .689546
        2 4  |   .6896981   .0425793    16.20   0.000     .6062441    .7731521
        2 5  |   .7403276   .0528518    14.01   0.000       .63674    .8439152
        3 1  |   .1604403    .020744     7.73   0.000     .1197827    .2010978
        3 2  |   .1665749   .0172189     9.67   0.000     .1328264    .2003234
        3 3  |    .167348     .02214     7.56   0.000     .1239544    .2107416
        3 4  |   .1635688   .0317989     5.14   0.000     .1012442    .2258934
        3 5  |   .1564184   .0423664     3.69   0.000     .0733819     .239455
------------------------------------------------------------------------------

. /* Reproduce Figure 1b */
> marginsplot, xdimension(distrust_local_officials) title(Acceptance of
> Corruption Rumors) ytitle(Predicted Probability)
> plot1opts(lpattern(solid) msymbol(O)) plot2opts(lpattern(dash)
> msymbol(T)) plot3opts(lpattern(dash) msymbol(Dh)) xlabel(1 "Low" 5
> "High") plotd(,label( "Deny" "Plausible" "Accept")) legend(row(1));

  Variables that uniquely identify margins: distrust_local_officials _outcome

.  graph export figures/figure1b.eps, as(eps) replace;
(note: file figures/figure1b.eps not found)
(file figures/figure1b.eps written in EPS format)

. /* Delete next two lines if not using linux */
> !epstopdf figures/figure1b.eps -o=figures/figure1b.pdf;


. /* Probability difference accepting corruption rumor between lowest
> and highest levels of distrust */
> lincom _b[3._predict#1bn._at] - _b[3._predict#5._at];

 ( 1)  3._predict#1bn._at - 3._predict#5._at = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0040218   .0516486     0.08   0.938    -.0972075    .1052512
------------------------------------------------------------------------------

. /* Reproduce Weber et al.'s (2014) results */
> /* First part of code comes directly from Weber et al.'s replication file */
> 
> #delimit cr
delimiter now cr
. clear

. 
. use "replication data.dta"

. 
. //Various Measures of Context//
. ************************************************************************
. ************************************************************************
. ************************************************************************
. ************************************************************************
. ************************************************************************
. //Heterogeneity Measure that is in the Paper//
. gen p1=(hispanic)+(black)+(asian)+(white)

. gen sum1=(hispanic^2)+(black^2)+(asian^2)+(white^2)

. gen het=p1-sum1

. set scheme s2mono

. graph twoway histogram het || kdensity het, bwidth(0.05) ytitle("density") xt
> itle("Heterogeneity, All Groups") legend(label(1 "Histogram") label(2 "Kernel
>  Density") )

. //A zero to 1 scale//
. replace het=(het-0.0178418)/.6732611 
(729 real changes made)

.  
. ////////////////
> //Black v. White Heterogeneity//
. //This is the measure in the paper, the rest are for the appendix//
. gen p2=(black)+(white)

. gen sum2=(black^2)+(white^2)

. gen het2=p2-sum2

. graph twoway histogram het2 || kdensity het2, bwidth(0.05) ytitle("density") 
> xtitle("Heterogeneity, Blacks/Whites") legend(label(1 "Histogram") label(2 "K
> ernel Density") )

. 
. //Just percentage black//
. graph twoway histogram black || kdensity black, bwidth(0.05) ytitle("density"
> ) xtitle("Percent Black") legend(label(1 "Histogram") label(2 "Kernel Density
> ") )

. 
. //Black:White//
. gen dif=(black)-(white)

. graph twoway histogram dif || kdensity dif, bwidth(0.05) ytitle("density") xt
> itle("Heterogeneity, Blacks-Whites") legend(label(1 "Histogram") label(2 "Smo
> othed Density") )

. *replace this to vary from 0 to 1
. sum dif

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
         dif |        729   -.7380576    .3020477  -.9870968   .7254366

. replace dif=(dif+ .9870968 )/1.712533
(729 real changes made)

. gen lndif=ln(dif+.01)

.   
.   
. ///Generate Don't Now and Midpoint responses for dissembling analysis////
> gen mid1=1 if workbla==5
(431 missing values generated)

. replace mid1=2 if workbla>5 & workbla<11
(258 real changes made)

. replace mid1=3 if workbla<5
(90 real changes made)

. replace mid1=4 if workbla>10
(83 real changes made)

. gen mid2=1 if violbla==5
(492 missing values generated)

. replace mid2=2 if violbla>5 & violbla<11
(238 real changes made)

. replace mid2=3 if violbla<5
(150 real changes made)

. replace mid2=4 if violbla>10
(104 real changes made)

. **Labels high scores are reject for the working stereotype, but endorse black
> s as violent for the violent stereotype** 
. label define mid1 1 "midpoint" 2 "reject" 3 "endorse" 4 "opt out"

. label values mid1 mid1

. label define mid2 1 "midpoint" 2 "endorse" 3 "reject" 4 "opt out"

. label values mid2 mid2

. **Recode so that dissembling is reject, midpoint, or opt out.
. recode mid1 (1 2 4=1) (3=0), gen(dissemble1)
(431 differences between mid1 and dissemble1)

. recode mid2 (1 3 4=1) (2=0), gen(dissemble2)
(492 differences between mid2 and dissemble2)

. 
. ************************************************************
. ************************************************************
. ************************************************************
. 
. //Independent variables//
. 
. ************************************************************
. ************************************************************
. ************************************************************
. 
. //egalitarianism scale//
. recode eqright (5 6=.), gen(eqright_r)
(24 differences between eqright and eqright_r)

. recode eqchanc (1=4) (2=3) (3=2) (4=1)  (5 6=.), gen(eqchanc_r)
(729 differences between eqchanc and eqchanc_r)

. recode eqpeopl (5 6=.), gen(eqpeopl_r)
(30 differences between eqpeopl and eqpeopl_r)

. replace eqright_r=(eqright_r-1)/3
(705 real changes made)

. replace eqchanc_r=(eqchanc_r-1)/3
(709 real changes made)

. replace eqpeopl_r=(eqpeopl_r-1)/3
(699 real changes made)

. egen egalitarianism=rmean(eqright_r eqpeopl_r)
(6 missing values generated)

. label variable eqright_r "eqright reversed 0-1"

. label variable eqpeopl_r "eqpeopl reversed 0-1"

. label variable eqchanc_r "eqchanc 0-1"

. 
. 
. 
. //individualism scale//
. recode blame (1=4) (2=3) (3=2) (4=1) (5 6=.), gen(blame_r)
(729 differences between blame and blame_r)

. recode hardwrk  (5 6=.), gen(hardwrk_r)
(14 differences between hardwrk and hardwrk_r)

. recode poverty (1=3) (2=1) (3=2) (4=2) (5 6=.), gen(poverty_r)
(729 differences between poverty and poverty_r)

. replace blame_r=(blame_r-1)/3
(699 real changes made)

. replace hardwrk_r=(hardwrk_r-1)/3
(715 real changes made)

. replace poverty_r=(poverty_r-1)/3
(687 real changes made)

. alpha blame_r poverty_r

Test scale = mean(unstandardized items)

Average interitem covariance:     .0316756
Number of items in the scale:            2
Scale reliability coefficient:      0.4878

. egen individualism=rmean(blame_r poverty_r)
(3 missing values generated)

. label variable blame_r "blame reversed, 0-1"

. label variable hardwrk_r "hardwrk 0-1"

. label variable poverty_r "poverty reversed, 0-1"

. 
. 
. //PID and Ideology//
. **High scores are republicans and conservatives**
. gen pid=.
(729 missing values generated)

. replace pid=1 if strongr==1
(98 real changes made)

. replace pid=.83 if strongr==2 | strongr==3
(99 real changes made)

. replace pid=.67 if closer==1
(100 real changes made)

. replace pid=.5 if closer==3 | closer==4
(104 real changes made)

. replace pid=.33 if closer==2
(101 real changes made)

. replace pid=.17 if strongd==2 | strongd==3
(103 real changes made)

. replace pid=0 if strongd==1
(124 real changes made)

. gen libcon=.
(729 missing values generated)

. replace libcon=1 if conserv==1
(73 real changes made)

. replace libcon=.83 if conserv==2 | conserv==3
(105 real changes made)

. replace libcon=.67 if moderat==2
(165 real changes made)

. replace libcon=.5 if moderat==3 | moderat==4 | moderat==5 | moderat==6
(62 real changes made)

. replace libcon=.33 if moderat==1
(120 real changes made)

. replace libcon=.17 if liberal==2 | liberal==3
(110 real changes made)

. replace libcon=0 if liberal== 1
(94 real changes made)

. 
. //self monitoring scale//
. recode i_expec (1=4) (2=3) (3=2) (4=1) (5 6=.), gen(i_expec_r)
(729 differences between i_expec and i_expec_r)

. recode i_amnot (1=4) (2=3) (3=2) (4=1) (5 6=.), gen(i_amnot_r )
(729 differences between i_amnot and i_amnot_r)

. recode i_nojoy (1=4) (2=3) (3=2) (4=1) (5 6=.), gen(i_nojoy_r)
(729 differences between i_nojoy and i_nojoy_r)

. recode i_deciv (1=4) (2=3) (3=2) (4=1) (5 6=.), gen(i_deciv_r )
(729 differences between i_deciv and i_deciv_r)

. replace i_expec_r=(i_expec_r-1)/3
(716 real changes made)

. replace i_amnot_r=(i_amnot_r-1)/3
(716 real changes made)

. replace i_nojoy_r=(i_nojoy_r-1)/3
(719 real changes made)

. replace i_deciv_r=(i_deciv_r-1)/3
(716 real changes made)

. alpha i_expec_r i_amnot_r i_nojoy_r i_deciv_r

Test scale = mean(unstandardized items)

Average interitem covariance:     .0297679
Number of items in the scale:            4
Scale reliability coefficient:      0.6414

. egen smonitor=rmean(i_expec_r i_amnot_r i_nojoy_r i_deciv_r)
(5 missing values generated)

. label variable i_expec_r "i_expec reverse 0-1"

. label variable i_amnot_r "i_amnot reverse 0-1"

. label variable i_nojoy_r "i_nojoy reverse 0-1"

. label variable i_deciv_r "i_deciv reverse 0-1"

. 
. 
. //stereotypes towards blacks//
. replace workbla=. if workbla>10
(83 real changes made, 83 to missing)

. replace violbla=. if violbla>10
(104 real changes made, 104 to missing)

. gen workbla0=((11-workbla)-1)/9
(83 missing values generated)

. gen violbla0=(violbla-1)/9
(104 missing values generated)

. //welfare and poverty stereotypes for  blacks
. gen welfbla0=welfarb

. replace welfbla0=. if welfbla0>10
(100 real changes made, 100 to missing)

. replace welfbla0=(welfbla0-1)/9
(629 real changes made)

. replace wealthb=. if wealthb>10
(61 real changes made, 61 to missing)

. gen poorbla0=((11-wealthb)-1)/9
(61 missing values generated)

. 
. 
. //stereotypes towards whites
. replace workwhi=. if workwhi>10
(80 real changes made, 80 to missing)

. replace violwhi=. if violwhi>10
(102 real changes made, 102 to missing)

. gen workwhi0=((11-workwhi)-1)/9
(80 missing values generated)

. gen violwhi0=(violwhi-1)/9
(102 missing values generated)

. 
. 
. //Generate several "difference" measures//
. gen d1_violent=violbla0-violwhi0
(109 missing values generated)

. gen d1_work=workbla0-workwhi0
(87 missing values generated)

. reg violbla0 violwhi0

      Source |       SS           df       MS      Number of obs   =       620
-------------+----------------------------------   F(1, 618)       =    512.18
       Model |   10.817992         1   10.817992   Prob > F        =    0.0000
    Residual |  13.0530565       618  .021121451   R-squared       =    0.4532
-------------+----------------------------------   Adj R-squared   =    0.4523
       Total |  23.8710486       619  .038563891   Root MSE        =    .14533

------------------------------------------------------------------------------
    violbla0 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    violwhi0 |   .6852495   .0302787    22.63   0.000     .6257879    .7447112
       _cons |   .1782345   .0143208    12.45   0.000     .1501113    .2063578
------------------------------------------------------------------------------

. predict d2_violent, residuals
(109 missing values generated)

. reg workbla0 workwhi0

      Source |       SS           df       MS      Number of obs   =       642
-------------+----------------------------------   F(1, 640)       =    244.93
       Model |  4.65899872         1  4.65899872   Prob > F        =    0.0000
    Residual |  12.1741435       640  .019022099   R-squared       =    0.2768
-------------+----------------------------------   Adj R-squared   =    0.2756
       Total |  16.8331422       641  .026260752   Root MSE        =    .13792

------------------------------------------------------------------------------
    workbla0 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    workwhi0 |   .4876952   .0311624    15.65   0.000     .4265022    .5488882
       _cons |   .2906557   .0139922    20.77   0.000     .2631796    .3181318
------------------------------------------------------------------------------

. predict d2_work, residuals
(87 missing values generated)

. 
. 
. 
. //Age//
. replace yearbrn=. if yearbrn>1983
(25 real changes made, 25 to missing)

. gen age=2001-yearbrn
(25 missing values generated)

. gen agemiss=age==.

. 
. 
. 
. 
. //Other Demographic Variables
. *Education is 1 if some college or greater
. recode educ 1 2 3 4 5 6 7 = 0 8=0 9 10=0 11=1 12 13 14=1 15 16=.
(educ: 729 changes made)

. *1 is female
. recode gender 2=0 3=.
(gender: 320 changes made)

. 
. //racial resentment
. gen irish_r=irish

. gen tryhard_r =tryhard

. gen deserve_r=deserve

. gen slavery_r=slavery

. recode irish_r 1=1 2=.75 5 6=.5 3=.25 4=0
(irish_r: 500 changes made)

. recode tryhard_r 1=1 2=.75 5 6=.5 3=.25 4=0
(tryhard_r: 586 changes made)

. recode deserve_r 1=0 2=.25 5 6=.5 3=.75 4=1
(deserve_r: 729 changes made)

. recode slavery_r 1=0 2=.25 5 6=.5 3=.75 4=1
(slavery_r: 729 changes made)

. gen discrim_r=discrim

. gen noeduc_r=noeduc

. replace discrim_r=1-discrim_r
(729 real changes made)

. replace noeduc_r=1-noeduc_r
(729 real changes made)

. egen resent4= rmean (irish_r tryhard_r deserve_r slavery_r)

. label variable resent4 "K & S 4-item resetnment scale" 

. 
. ************************************************************
. ************************************************************
. ************************************************************
. 
. //Dependent variables//
. 
. ************************************************************
. ************************************************************
. ************************************************************
. 
. 
. //Affirmative Action
. recode homeown 3 4=.
(homeown: 29 changes made)

. recode integr 5 6=.
(integr: 54 changes made)

. recode locgov 5 6=.
(locgov: 60 changes made)

. recode spended 5 6=.
(spended: 57 changes made)

. recode affact1 3 4=.
(affact1: 96 changes made)

. recode affact2 3 4=.
(affact2: 113 changes made)

. gen affact1_01=affact1
(96 missing values generated)

. recode affact1_01 1=0 2=1
(affact1_01: 633 changes made)

. gen affact2_01=affact2
(113 missing values generated)

. recode affact2_01 1=0 2=1
(affact2_01: 616 changes made)

. egen affact=rmean(affact1_01 affact2_01)
(45 missing values generated)

. label variable affact "affact1 afact2 0-1: 1=oppose aff action"

. 
. 
. // Housing integration
. recode profil1 5 6=.
(profil1: 37 changes made)

. recode profil2 5 6=.
(profil2: 23 changes made)

. recode profil3 5 6=.
(profil3: 32 changes made)

. recode deathpe 5 6=.
(deathpe: 34 changes made)

. recode favordp 3 4=.
(favordp: 44 changes made)

. recode suspend 4 5=.
(suspend: 59 changes made)

. recode nylaw 3 4=.
(nylaw: 35 changes made)

. recode innocen 3 4=. 
(innocen: 107 changes made)

. recode helpblc 3 4=.
(helpblc: 133 changes made)

. gen homeown01=homeown
(29 missing values generated)

. recode homeown01 1=0 2=1
(homeown01: 700 changes made)

. label variable homeown0 "0-1 homeown, 1=up to homeowner"

. gen integr01=integr
(54 missing values generated)

. recode integr01 1=0 2=.33 3=.67 4=1
(integr01: 675 changes made)

. label variable integr01 "integr recoded to 0-1, 1=st. oppose"

. gen locgov01=locgov
(60 missing values generated)

. recode locgov01 1=0 2=.33 3=.67 4=1
(locgov01: 669 changes made)

. label variable locgov01 "locgov: recoded to 0-1, 1=st oppose"

. egen housintegr=rmean (integr01 locgov01 homeown01)
(4 missing values generated)

. egen housintegr2=rmean(integr01 locgov01)
(24 missing values generated)

. label variable housintegr2 "housing integration -integr01 locgov01"

. 
. 
. //Aid to blacks
. gen helpblk01=helpblc
(133 missing values generated)

. recode helpblk01 1=0 2=1
(helpblk01: 596 changes made)

. gen spended01=spended
(57 missing values generated)

. recode spended01 1=0 2=.33 3=.67 4=1
(spended01: 672 changes made)

. egen aidblk=rmean(helpblk01 spended01)
(29 missing values generated)

. 
. //Racial Profiling
. gen profil2_01=profil2
(23 missing values generated)

. gen profil3_01=profil3
(32 missing values generated)

. recode profil2_01 1=0 2=.33 3=.67 4=1
(profil2_01: 706 changes made)

. recode profil3_01 1=0 2=.33 3=.67 4=1
(profil3_01: 697 changes made)

. egen rac_profil=rmean(profil2_01 profil3_01)
(10 missing values generated)

. label variable rac_profil "oppose action against officers: profiling" 

. 
. //Death Penalty Attitudes
. recode deathpe 4=0 3=.33 2=.67 1=1 5=. 6=.
(deathpe: 394 changes made)

. recode favordp 2=0 1=1 3=. 4=.
(favordp: 133 changes made)

. recode suspend 1=0 2=1 3=. 4=. 5=.
(suspend: 670 changes made)

. recode nylaw 2=0 1=1 3=. 4=.
(nylaw: 231 changes made)

. recode innocen 2=0 1=1 3=. 4=.
(innocen: 308 changes made)

. egen death_penalty=rmean(deathpe suspend nylaw innocen)
(9 missing values generated)

. label variable death_penalty "death penalty: deathpe suspend nylaw innoc"

. alpha integr01 locgov01 helpblk01 spended01 affact1_01 affact2_01, std gen(dv
> 1)

Test scale = mean(standardized items)

Average interitem correlation:      0.3471
Number of items in the scale:            6
Scale reliability coefficient:      0.7613

. egen dv_1=rmean(integr01 locgov01 helpblk01 spended01 affact1_01 affact2_01)
(5 missing values generated)

. 
. 
. //Subjective Diversity
. gen pcent=pct_aa
(237 missing values generated)

. replace pcent=0 if nxtdoor==2
(212 real changes made)

. recode pcent (999 998 111=.)
(pcent: 83 changes made)

. 
. //Media consumption
. gen local=localtv

. replace local=. if local==8
(5 real changes made, 5 to missing)

. 
. /* Skip creation of wave variables that require area and areacode */
. 
. //Primary Model includes %B-%W
. //Do not select below for figure. Scroll to **end**//
. 
. ******BEGIN**********
. ***First, Mean Center Everything*****
. egen EGALITARIANISM=mean(egalitarianism) 

. egen INDIVIDUALISM=mean(individualism) 

. egen PID=mean(pid)

. egen LIBCON=mean(libcon) 

. egen WORKBLA0=mean(workbla0) 

. egen VIOLBLA0=mean(violbla0) 

. egen SMONITOR=mean(smonitor) 

. egen DIF=mean(dif)

. egen LNDIF=mean(lndif)

. egen HET=mean(het)

. egen LOCAL=mean(local)

. 
. replace egalitarianism=egalitarianism-EGALITARIANISM 
(723 real changes made)

. replace individualism=individualism-INDIVIDUALISM 
(726 real changes made)

. replace pid=pid-PID 
(729 real changes made)

. replace libcon=libcon-LIBCON 
(729 real changes made)

. replace workbla0=workbla0-WORKBLA0 
(646 real changes made)

. replace violbla0=violbla0-VIOLBLA0 
(625 real changes made)

. replace smonitor=smonitor-SMONITOR 
(724 real changes made)

. replace dif=dif-DIF
(729 real changes made)

. replace lndif=lndif-LNDIF
(729 real changes made)

. replace het=het-HET
(729 real changes made)

. replace local=local-LOCAL
(724 real changes made)

. 
. //Correlation of stereotypes across levels of self monitoring
. corr workbla0 violbla0 if smonitor> .1246931  & smonitor<.
(obs=126)

             | workbla0 violbla0
-------------+------------------
    workbla0 |   1.0000
    violbla0 |  -0.1777   1.0000


. corr workbla0 violbla0 if  smonitor<-.1253069 
(obs=230)

             | workbla0 violbla0
-------------+------------------
    workbla0 |   1.0000
    violbla0 |   0.1335   1.0000


. corr workbla0 violbla0 if smonitor>-.1252  & smonitor<.125  
(obs=257)

             | workbla0 violbla0
-------------+------------------
    workbla0 |   1.0000
    violbla0 |  -0.0444   1.0000


. 
. //summary measures
. alpha integr01 locgov01 helpblk01 spended01 affact1_01 affact2_01

Test scale = mean(unstandardized items)

Average interitem covariance:     .0552906
Number of items in the scale:            6
Scale reliability coefficient:      0.7513

. alpha deathpe suspend nylaw innocen

Test scale = mean(unstandardized items)

Average interitem covariance:     .1498603
Number of items in the scale:            4
Scale reliability coefficient:      0.8969

. alpha irish_r tryhard_r deserve_r slavery_r

Test scale = mean(unstandardized items)

Average interitem covariance:     .0453669
Number of items in the scale:            4
Scale reliability coefficient:      0.6907

. corr blame_r poverty_r
(obs=660)

             |  blame_r povert~r
-------------+------------------
     blame_r |   1.0000
   poverty_r |   0.3229   1.0000


. corr eqright_r eqpeopl_r
(obs=681)

             | eqrigh~r eqpeop~r
-------------+------------------
   eqright_r |   1.0000
   eqpeopl_r |   0.3142   1.0000


.  
.  
. /*** creating interactions with self monitoring and neighborhood composition*
> */
. //model with difference black-white//
. local h="dif"

. gen ZIPbXsm=`h'*smonitor
(5 missing values generated)

. gen workblaXsm=workbla0*smonitor
(85 missing values generated)

. gen violblaXsm=violbla0*smonitor
(106 missing values generated)

. gen workblaXZIPb=workbla0*`h'
(83 missing values generated)

. gen violblaXZIPb=violbla0*`h'
(104 missing values generated)

. gen workXsmXZIP=workbla0*`h'*smonitor
(85 missing values generated)

. gen vioXsmXZIP=violbla0*`h'*smonitor
(106 missing values generated)

. 
. /* Stop briefly to check ZIPbXsm */
. sum ZIPbXsm

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
     ZIPbXsm |        724    .0024827    .0461637  -.2188147   .6050643

. 
. /* ** line added to use random number generator for imputation that was used 
> for the version of Stata that Weber, et al. (2014) used ** */
. set rng kiss32

. 
. //Impute Age. Linear projection of covariates. Things do not substantively ch
> ange when interactions and dvs are included
. mi query
(data not mi set)

. mi set mlong

. mi register imputed age 
(25 m=0 obs. now marked as incomplete)

. mi register regular egalitarianism individualism  pid libcon  workbla0 violbl
> a0 gender educ smonitor dif 

. mi impute mvn age  =  egalitarianism individualism  pid libcon  workbla0 viol
> bla0 gender educ smonitor dif , add(1) rseed(546) force 

Performing EM optimization:
note: 18 observations omitted from EM estimation because of all imputation
      variables missing
  observed log likelihood = -1916.4237 at iteration 1

Performing MCMC data augmentation ... 

Multivariate imputation                     Imputations =        1
Multivariate normal regression                    added =        1
Imputed: m=1                                    updated =        0

Prior: uniform                               Iterations =      100
                                                burn-in =      100
                                                between =      100

------------------------------------------------------------------
                   |               Observations per m             
                   |----------------------------------------------
          Variable |   Complete   Incomplete   Imputed |     Total
-------------------+-----------------------------------+----------
               age |        704           25        18 |       729
------------------------------------------------------------------
(complete + incomplete = total; imputed is the minimum across m
 of the number of filled-in observations.)

Note: Right-hand-side variables (or weights) have missing values;
      model parameters estimated using listwise deletion.

. 
. //Mean center after imputation
. egen AGE=mean(age)

. replace age=age-AGE
(722 real changes made)

. 
. //Page 16 of Supplementary Material
. sum dif, detail

                             dif
-------------------------------------------------------------
      Percentiles      Smallest
 1%    -.1428507      -.1454215
 5%    -.1382369      -.1440466
10%    -.1350976       -.143781       Obs                 754
25%    -.1142281       -.143781       Sum of Wgt.         754

50%    -.0628936                      Mean           .0001347
                        Largest       Std. Dev.      .1757511
75%      .025267       .7539819
90%     .2360098       .7539819       Variance       .0308885
95%     .3836551       .7539819       Skewness       2.196291
99%     .7198153       .8545787       Kurtosis        8.18421

. gen hiD=1 if dif>=.2360098   & dif<.
(678 missing values generated)

. replace hiD=0 if dif<=-.1350976  
(76 real changes made)

. 
. sum smonitor, detail

                          smonitor
-------------------------------------------------------------
      Percentiles      Smallest
 1%    -.2919736      -.2919736
 5%    -.2919736      -.2919736
10%    -.2919736      -.2919736       Obs                 745
25%    -.1253069      -.2919736       Sum of Wgt.         745

50%    -.0419736                      Mean             .00262
                        Largest       Std. Dev.      .2213243
75%     .1246931       .7080264
90%     .2913598       .7080264       Variance       .0489844
95%     .3746931       .7080264       Skewness        .761099
99%     .7080264       .7080264       Kurtosis       3.400993

. gen hiS=1 if smonitor>=.2913598      & smonitor<.
(689 missing values generated)

. replace hiS=0 if smonitor<= -0.2919736
(102 real changes made)

. 
. by hiD, sort: sum  egalitarianism individualism pid libcon  workbla0 violbla0
>  gender educ smonitor age dif

-------------------------------------------------------------------------------
-> hiD = 0

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
egalitaria~m |         75   -.0872568     .288467  -.5472568   .4527432
individual~m |         76    .0154294    .2630234  -.4100092   .4233242
         pid |         76    .0984292    .3277385  -.4801235   .5198765
      libcon |         76    .0180149    .3229477  -.4938272   .5061728
    workbla0 |         69   -.0117009      .18815  -.4915721   .3973168
-------------+---------------------------------------------------------
    violbla0 |         65   -.0233846    .2150554  -.4746667   .4142222
      gender |         76    .6842105    .4679181          0          1
        educ |         76    .2105263    .4103913          0          1
    smonitor |         76    .0307603    .2474964  -.2919736   .7080264
         age |         73    .4907966    16.96209  -27.08829   33.91171
-------------+---------------------------------------------------------
         dif |         76   -.1385745    .0027053  -.1454215  -.1350976

-------------------------------------------------------------------------------
-> hiD = 1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
egalitaria~m |         76   -.0845375    .2783532  -.5472568   .4527432
individual~m |         76    .0000785     .286021  -.4100092   .4233242
         pid |         76   -.0107814    .3599197  -.4801235   .5198765
      libcon |         76    .0210413    .3160021  -.4938272   .5061728
    workbla0 |         67    .0341327    .2123938  -.4915721   .5084279
-------------+---------------------------------------------------------
    violbla0 |         66    .0404848    .2236165  -.4746667   .5253333
      gender |         75         .52    .5029642          0          1
        educ |         76    .3815789    .4890018          0          1
    smonitor |         76    .0398977      .26348  -.2919736   .7080264
         age |         73   -.9350413    17.59929  -29.08829   54.91171
-------------+---------------------------------------------------------
         dif |         76    .4336576    .1673767   .2360098   .8545787

-------------------------------------------------------------------------------
-> hiD = .

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
egalitaria~m |        597    .0161156    .2933744  -.5472568   .4527432
individual~m |        598   -.0028186    .2637938  -.4100092   .5899908
         pid |        602   -.0047248    .3349998  -.4801235   .5198765
      libcon |        602   -.0052391    .3147703  -.4938272   .5061728
    workbla0 |        531    .0007904    .1537058  -.4915721   .5084279
-------------+---------------------------------------------------------
    violbla0 |        514   -.0036334    .1919847  -.4746667   .5253333
      gender |        599     .557596    .4970867          0          1
        educ |        595     .497479    .5004143          0          1
    smonitor |        593   -.0057641    .2113192  -.2919736   .7080264
         age |        576    .0562996    16.67978  -30.84789   40.91171
-------------+---------------------------------------------------------
         dif |        602   -.0370843    .0872729  -.1349249   .2339163


. 
. /* Comment out this section 
> 
> //Also for supplement
> by zip, sort: gen count=_n
> drop if count!=1
> drop if hiD==.
> sort hiD zip
> keep hiD zip
> 
>  */
. 
. /* Skip down to models for Tables 3 and 4 */
. /* Change ZIPbXsm with c.smonitor#c.dif */
. 
. //Dissembling Table Model (Reported in Tables 3 and 4)
. mlogit mid1  egalitarianism individualism pid libcon gender educ smonitor age
>  dif ZIPbXsm, base(3)

Iteration 0:   log likelihood =  -869.9507  
Iteration 1:   log likelihood = -838.16185  
Iteration 2:   log likelihood = -836.04047  
Iteration 3:   log likelihood = -836.03184  
Iteration 4:   log likelihood = -836.03184  

Multinomial logistic regression                 Number of obs     =        707
                                                LR chi2(30)       =      67.84
                                                Prob > chi2       =     0.0001
Log likelihood = -836.03184                     Pseudo R2         =     0.0390

------------------------------------------------------------------------------
        mid1 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
midpoint     |
egalitaria~m |   .5229919    .485912     1.08   0.282    -.4293782    1.475362
individual~m |  -1.266503    .537138    -2.36   0.018    -2.319274   -.2137318
         pid |    .150418   .4274873     0.35   0.725    -.6874418    .9882778
      libcon |   -.178463   .4493004    -0.40   0.691    -1.059076    .7021497
      gender |   .0514769   .2567778     0.20   0.841    -.4517982    .5547521
        educ |  -.0263167   .2583946    -0.10   0.919    -.5327608    .4801274
    smonitor |  -1.170485   .5579906    -2.10   0.036    -2.264127   -.0768435
         age |  -.0077924   .0074948    -1.04   0.298     -.022482    .0068972
         dif |  -.8385858   .6844067    -1.23   0.220    -2.179998    .5028267
     ZIPbXsm |   3.445994   2.498849     1.38   0.168     -1.45166    8.343649
       _cons |   1.304022    .230106     5.67   0.000     .8530223    1.755021
-------------+----------------------------------------------------------------
reject       |
egalitaria~m |   .8629276   .4958026     1.74   0.082    -.1088275    1.834683
individual~m |  -1.179595   .5467402    -2.16   0.031    -2.251186   -.1080035
         pid |   -.330842   .4352396    -0.76   0.447    -1.183896    .5222119
      libcon |   .0459618   .4589484     0.10   0.920    -.8535605    .9454841
      gender |  -.0167312   .2614119    -0.06   0.949    -.5290892    .4956267
        educ |  -.0871954   .2636855    -0.33   0.741    -.6040095    .4296187
    smonitor |  -.7016442   .5656917    -1.24   0.215     -1.81038    .4070912
         age |  -.0059822   .0076223    -0.78   0.433    -.0209216    .0089572
         dif |   -.688918   .6923559    -1.00   0.320    -2.045911    .6680745
     ZIPbXsm |   1.318872   2.680161     0.49   0.623    -3.934147    6.571892
       _cons |   1.227724   .2328758     5.27   0.000     .7712961    1.684152
-------------+----------------------------------------------------------------
endorse      |  (base outcome)
-------------+----------------------------------------------------------------
opt_out      |
egalitaria~m |   2.185032    .678149     3.22   0.001     .8558843     3.51418
individual~m |   -1.96986    .731432    -2.69   0.007    -3.403441   -.5362799
         pid |   .3500255   .5957573     0.59   0.557    -.8176373    1.517688
      libcon |  -.5000264   .6253153    -0.80   0.424    -1.725622    .7255691
      gender |  -.2591171   .3392049    -0.76   0.445    -.9239465    .4057123
        educ |   .6707663   .3464384     1.94   0.053    -.0082405    1.349773
    smonitor |  -1.777532   .7967695    -2.23   0.026    -3.339172   -.2158925
         age |  -.0171064   .0104171    -1.64   0.101    -.0375236    .0033108
         dif |  -.2731976   .9114685    -0.30   0.764    -2.059643    1.513248
     ZIPbXsm |   5.331035   3.502749     1.52   0.128    -1.534227     12.1963
       _cons |  -.4541071   .3215333    -1.41   0.158    -1.084301    .1760866
------------------------------------------------------------------------------

. mlogit mid2 egalitarianism individualism   pid libcon gender educ smonitor ag
> e  dif ZIPbXsm, base(2)

Iteration 0:   log likelihood = -939.15682  
Iteration 1:   log likelihood = -903.82669  
Iteration 2:   log likelihood = -903.05897  
Iteration 3:   log likelihood = -903.05624  
Iteration 4:   log likelihood = -903.05624  

Multinomial logistic regression                 Number of obs     =        707
                                                LR chi2(30)       =      72.20
                                                Prob > chi2       =     0.0000
Log likelihood = -903.05624                     Pseudo R2         =     0.0384

------------------------------------------------------------------------------
        mid2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
midpoint     |
egalitaria~m |   .4164519   .3701857     1.12   0.261    -.3090988    1.142003
individual~m |  -.1095046   .4058176    -0.27   0.787    -.9048924    .6858832
         pid |    .280696   .3339303     0.84   0.401    -.3737955    .9351874
      libcon |  -.0746433   .3516159    -0.21   0.832    -.7637978    .6145112
      gender |   .3551702   .1964073     1.81   0.071    -.0297809    .7401214
        educ |   .2964652    .197825     1.50   0.134    -.0912646    .6841951
    smonitor |  -.0442507   .4419366    -0.10   0.920    -.9104305    .8219291
         age |   .0000225   .0058725     0.00   0.997    -.0114874    .0115324
         dif |  -1.318404   .5662597    -2.33   0.020    -2.428253   -.2085559
     ZIPbXsm |    4.26232   2.590878     1.65   0.100    -.8157063    9.340347
       _cons |   -.314098   .1711929    -1.83   0.067    -.6496298    .0214339
-------------+----------------------------------------------------------------
endorse      |  (base outcome)
-------------+----------------------------------------------------------------
reject       |
egalitaria~m |  -.0481369   .4202915    -0.11   0.909    -.8718931    .7756193
individual~m |   .4121302   .4680687     0.88   0.379    -.5052676    1.329528
         pid |   .4984703   .3819747     1.30   0.192    -.2501864    1.247127
      libcon |  -.9311416   .3980857    -2.34   0.019    -1.711375   -.1509079
      gender |   .0493415   .2228306     0.22   0.825    -.3873984    .4860814
        educ |   .5842171   .2263391     2.58   0.010     .1406006    1.027834
    smonitor |  -.0302785   .5038566    -0.06   0.952    -1.017819    .9572623
         age |   .0171665    .006649     2.58   0.010     .0041346    .0301983
         dif |  -1.844824   .7456188    -2.47   0.013     -3.30621   -.3834383
     ZIPbXsm |   8.306253   2.788529     2.98   0.003     2.840837    13.77167
       _cons |  -.7896602   .1976756    -3.99   0.000    -1.177097   -.4022233
-------------+----------------------------------------------------------------
opt_out      |
egalitaria~m |   1.385517   .5118665     2.71   0.007      .382277    2.388757
individual~m |  -.2485439   .5474406    -0.45   0.650    -1.321508      .82442
         pid |   .6886727   .4570774     1.51   0.132    -.2071826    1.584528
      libcon |  -.5977616    .479043    -1.25   0.212    -1.536669    .3411456
      gender |   .1082129   .2601412     0.42   0.677    -.4016545    .6180803
        educ |   .6567665   .2625849     2.50   0.012     .1421097    1.171423
    smonitor |  -1.336311   .6396135    -2.09   0.037     -2.58993   -.0826917
         age |   .0094026   .0079035     1.19   0.234    -.0060881    .0248932
         dif |  -.0513935   .6921822    -0.07   0.941    -1.408046    1.305259
     ZIPbXsm |   6.895802   3.092575     2.23   0.026      .834466    12.95714
       _cons |  -1.352386   .2386408    -5.67   0.000    -1.820113   -.8846581
------------------------------------------------------------------------------

. 
. /* New code for the Paolino supplement begins here */
. /* First mlogit command shows that interaction reproduces results from Weber,
>  et al.'s (2014) code */
. 
. mlogit mid1  egalitarianism individualism pid libcon gender educ smonitor age
>  dif c.smonitor#c.dif, base(3)

Iteration 0:   log likelihood =  -869.9507  
Iteration 1:   log likelihood = -838.16185  
Iteration 2:   log likelihood = -836.04047  
Iteration 3:   log likelihood = -836.03184  
Iteration 4:   log likelihood = -836.03184  

Multinomial logistic regression                 Number of obs     =        707
                                                LR chi2(30)       =      67.84
                                                Prob > chi2       =     0.0001
Log likelihood = -836.03184                     Pseudo R2         =     0.0390

------------------------------------------------------------------------------
        mid1 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
midpoint     |
egalitaria~m |   .5229919    .485912     1.08   0.282    -.4293782    1.475362
individual~m |  -1.266503    .537138    -2.36   0.018    -2.319274   -.2137318
         pid |    .150418   .4274873     0.35   0.725    -.6874418    .9882778
      libcon |   -.178463   .4493004    -0.40   0.691    -1.059076    .7021497
      gender |   .0514769   .2567778     0.20   0.841    -.4517982    .5547521
        educ |  -.0263167   .2583946    -0.10   0.919    -.5327608    .4801274
    smonitor |  -1.170485   .5579906    -2.10   0.036    -2.264127   -.0768435
         age |  -.0077924   .0074948    -1.04   0.298     -.022482    .0068972
         dif |  -.8385858   .6844067    -1.23   0.220    -2.179998    .5028266
             |
  c.smonitor#|
       c.dif |   3.445994   2.498849     1.38   0.168     -1.45166    8.343649
             |
       _cons |   1.304022    .230106     5.67   0.000     .8530223    1.755021
-------------+----------------------------------------------------------------
reject       |
egalitaria~m |   .8629276   .4958026     1.74   0.082    -.1088275    1.834683
individual~m |  -1.179595   .5467402    -2.16   0.031    -2.251186   -.1080035
         pid |   -.330842   .4352396    -0.76   0.447    -1.183896    .5222119
      libcon |   .0459618   .4589484     0.10   0.920    -.8535605    .9454841
      gender |  -.0167312   .2614119    -0.06   0.949    -.5290892    .4956267
        educ |  -.0871954   .2636855    -0.33   0.741    -.6040095    .4296187
    smonitor |  -.7016442   .5656917    -1.24   0.215     -1.81038    .4070912
         age |  -.0059822   .0076223    -0.78   0.433    -.0209216    .0089572
         dif |   -.688918   .6923559    -1.00   0.320    -2.045911    .6680745
             |
  c.smonitor#|
       c.dif |   1.318872   2.680161     0.49   0.623    -3.934147    6.571892
             |
       _cons |   1.227724   .2328758     5.27   0.000     .7712961    1.684152
-------------+----------------------------------------------------------------
endorse      |  (base outcome)
-------------+----------------------------------------------------------------
opt_out      |
egalitaria~m |   2.185032    .678149     3.22   0.001     .8558843     3.51418
individual~m |   -1.96986    .731432    -2.69   0.007    -3.403441   -.5362799
         pid |   .3500255   .5957573     0.59   0.557    -.8176373    1.517688
      libcon |  -.5000264   .6253153    -0.80   0.424    -1.725622    .7255691
      gender |  -.2591171   .3392049    -0.76   0.445    -.9239465    .4057123
        educ |   .6707663   .3464384     1.94   0.053    -.0082405    1.349773
    smonitor |  -1.777532   .7967695    -2.23   0.026    -3.339172   -.2158925
         age |  -.0171064   .0104171    -1.64   0.101    -.0375236    .0033108
         dif |  -.2731976   .9114685    -0.30   0.764    -2.059643    1.513248
             |
  c.smonitor#|
       c.dif |   5.331035   3.502749     1.52   0.128    -1.534227     12.1963
             |
       _cons |  -.4541071   .3215333    -1.41   0.158    -1.084301    .1760866
------------------------------------------------------------------------------

. 
. /* Predicted probabilities for lazy stereotype p.18 of the supplement */
. 
. margins, at((mean) _all gender=1 educ=0 (p10) dif (min) smonitor) at((mean) _
> all gender=1 educ=0 (p10) dif (max) smonitor) at((mean) _all gender=1 educ=0 
> (p90) dif (min) smonitor) at((mean) _all gender=1 educ=0 (p90) dif (max) smon
> itor) post

Adjusted predictions                            Number of obs     =        707
Model VCE    : OIM

1._predict   : Pr(mid1==midpoint), predict(pr outcome(1))
2._predict   : Pr(mid1==reject), predict(pr outcome(2))
3._predict   : Pr(mid1==endorse), predict(pr outcome(3))
4._predict   : Pr(mid1==opt_out), predict(pr outcome(4))

1._at        : egalitaria~m    =    .0008337 (mean)
               individual~m    =    .0001747 (mean)
               pid             =   -.0003922 (mean)
               libcon          =   -.0027522 (mean)
               gender          =           1
               educ            =           0
               smonitor        =   -.2919736 (min)
               age             =   -.2838429 (mean)
               dif             =   -.1351765 (p10)

2._at        : egalitaria~m    =    .0008337 (mean)
               individual~m    =    .0001747 (mean)
               pid             =   -.0003922 (mean)
               libcon          =   -.0027522 (mean)
               gender          =           1
               educ            =           0
               smonitor        =    .7080264 (max)
               age             =   -.2838429 (mean)
               dif             =   -.1351765 (p10)

3._at        : egalitaria~m    =    .0008337 (mean)
               individual~m    =    .0001747 (mean)
               pid             =   -.0003922 (mean)
               libcon          =   -.0027522 (mean)
               gender          =           1
               educ            =           0
               smonitor        =   -.2919736 (min)
               age             =   -.2838429 (mean)
               dif             =    .2360098 (p90)

4._at        : egalitaria~m    =    .0008337 (mean)
               individual~m    =    .0001747 (mean)
               pid             =   -.0003922 (mean)
               libcon          =   -.0027522 (mean)
               gender          =           1
               educ            =           0
               smonitor        =    .7080264 (max)
               age             =   -.2838429 (mean)
               dif             =    .2360098 (p90)

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_predict#_at |
        1 1  |   .5067855   .0473864    10.69   0.000     .4139099    .5996611
        1 2  |   .3083593   .0755432     4.08   0.000     .1602975    .4564212
        1 3  |   .4275104   .0615316     6.95   0.000     .3069106    .5481102
        1 4  |   .4152769   .0953521     4.36   0.000     .2283902    .6021636
        2 1  |   .3444208   .0444577     7.75   0.000     .2572853    .4315562
        2 2  |   .4464913   .0901599     4.95   0.000     .2697811    .6232014
        2 3  |   .3867737   .0610592     6.33   0.000     .2670999    .5064474
        2 4  |   .3634442   .0990645     3.67   0.000     .1692813    .5576071
        3 1  |   .0721405   .0204327     3.53   0.000     .0320932    .1121879
        3 2  |   .2254496   .0830238     2.72   0.007      .062726    .3881732
        3 3  |   .1206922   .0374706     3.22   0.001     .0472512    .1941332
        3 4  |    .167571   .0722105     2.32   0.020      .026041     .309101
        4 1  |   .0766532   .0235568     3.25   0.001     .0304827    .1228237
        4 2  |   .0196998   .0129457     1.52   0.128    -.0056733     .045073
        4 3  |   .0650237   .0252759     2.57   0.010     .0154838    .1145636
        4 4  |   .0537079   .0328797     1.63   0.102    -.0107351    .1181509
------------------------------------------------------------------------------

. 
. lincom _b[1bn._predict#4._at] - _b[1bn._predict#3._at]

 ( 1)  - 1bn._predict#3._at + 1bn._predict#4._at = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0122335   .1313784    -0.09   0.926    -.2697305    .2452635
------------------------------------------------------------------------------

. lincom _b[1bn._predict#2._at] - _b[1bn._predict#1._at]

 ( 1)  - 1bn._predict#1bn._at + 1bn._predict#2._at = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.1984262   .1020651    -1.94   0.052      -.39847    .0016177
------------------------------------------------------------------------------

. 
. lincom (_b[1bn._predict#4._at] - _b[1bn._predict#3._at]) - (_b[1bn._predict#2
> ._at] - _b[1bn._predict#1._at])

 ( 1)  1bn._predict#1bn._at - 1bn._predict#2._at - 1bn._predict#3._at +
       1bn._predict#4._at = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .1861927   .1566584     1.19   0.235    -.1208522    .4932375
------------------------------------------------------------------------------

. 
. lincom _b[2._predict#4._at] - _b[2._predict#3._at]

 ( 1)  - 2._predict#3._at + 2._predict#4._at = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0233295   .1353647    -0.17   0.863    -.2886394    .2419805
------------------------------------------------------------------------------

. lincom _b[2._predict#2._at] - _b[2._predict#1._at]

 ( 1)  - 2._predict#1bn._at + 2._predict#2._at = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .1020705   .1133156     0.90   0.368    -.1200241    .3241651
------------------------------------------------------------------------------

. 
. lincom (_b[2._predict#4._at] - _b[2._predict#3._at]) - (_b[2._predict#2._at] 
> - _b[2._predict#1._at])

 ( 1)  2._predict#1bn._at - 2._predict#2._at - 2._predict#3._at +
       2._predict#4._at = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |     -.1254   .1708285    -0.73   0.463    -.4602176    .2094177
------------------------------------------------------------------------------

. 
. lincom _b[3._predict#4._at] - _b[3._predict#3._at]

 ( 1)  - 3._predict#3._at + 3._predict#4._at = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0468788   .0913701     0.51   0.608    -.1322034    .2259609
------------------------------------------------------------------------------

. lincom _b[3._predict#2._at] - _b[3._predict#1._at]

 ( 1)  - 3._predict#1bn._at + 3._predict#2._at = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .1533091   .0917044     1.67   0.095    -.0264283    .3330464
------------------------------------------------------------------------------

. 
. lincom _b[4._predict#4._at] - _b[4._predict#3._at]

 ( 1)  - 4._predict#3._at + 4._predict#4._at = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0113158   .0465744    -0.24   0.808    -.1025999    .0799682
------------------------------------------------------------------------------

. lincom _b[4._predict#2._at] - _b[4._predict#1._at]

 ( 1)  - 4._predict#1bn._at + 4._predict#2._at = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0569534   .0293979    -1.94   0.053    -.1145722    .0006654
------------------------------------------------------------------------------

. 
. mlogit mid2 egalitarianism individualism   pid libcon gender educ smonitor ag
> e  dif c.smonitor#c.dif, base(2)

Iteration 0:   log likelihood = -939.15682  
Iteration 1:   log likelihood = -903.82669  
Iteration 2:   log likelihood = -903.05897  
Iteration 3:   log likelihood = -903.05624  
Iteration 4:   log likelihood = -903.05624  

Multinomial logistic regression                 Number of obs     =        707
                                                LR chi2(30)       =      72.20
                                                Prob > chi2       =     0.0000
Log likelihood = -903.05624                     Pseudo R2         =     0.0384

------------------------------------------------------------------------------
        mid2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
midpoint     |
egalitaria~m |   .4164519   .3701857     1.12   0.261    -.3090988    1.142003
individual~m |  -.1095046   .4058176    -0.27   0.787    -.9048924    .6858832
         pid |    .280696   .3339303     0.84   0.401    -.3737955    .9351874
      libcon |  -.0746433   .3516159    -0.21   0.832    -.7637978    .6145112
      gender |   .3551702   .1964073     1.81   0.071    -.0297809    .7401214
        educ |   .2964652    .197825     1.50   0.134    -.0912646    .6841951
    smonitor |  -.0442507   .4419366    -0.10   0.920    -.9104305    .8219291
         age |   .0000225   .0058725     0.00   0.997    -.0114874    .0115324
         dif |  -1.318404   .5662597    -2.33   0.020    -2.428253   -.2085559
             |
  c.smonitor#|
       c.dif |   4.262321   2.590878     1.65   0.100    -.8157062    9.340347
             |
       _cons |   -.314098   .1711929    -1.83   0.067    -.6496298    .0214339
-------------+----------------------------------------------------------------
endorse      |  (base outcome)
-------------+----------------------------------------------------------------
reject       |
egalitaria~m |  -.0481369   .4202915    -0.11   0.909    -.8718931    .7756193
individual~m |   .4121302   .4680687     0.88   0.379    -.5052676    1.329528
         pid |   .4984703   .3819747     1.30   0.192    -.2501864    1.247127
      libcon |  -.9311416   .3980857    -2.34   0.019    -1.711375   -.1509079
      gender |   .0493415   .2228306     0.22   0.825    -.3873984    .4860815
        educ |   .5842171   .2263391     2.58   0.010     .1406006    1.027834
    smonitor |  -.0302785   .5038566    -0.06   0.952    -1.017819    .9572623
         age |   .0171665    .006649     2.58   0.010     .0041346    .0301983
         dif |  -1.844824   .7456188    -2.47   0.013     -3.30621   -.3834383
             |
  c.smonitor#|
       c.dif |   8.306253   2.788529     2.98   0.003     2.840837    13.77167
             |
       _cons |  -.7896602   .1976756    -3.99   0.000    -1.177097   -.4022233
-------------+----------------------------------------------------------------
opt_out      |
egalitaria~m |   1.385517   .5118665     2.71   0.007      .382277    2.388757
individual~m |  -.2485439   .5474406    -0.45   0.650    -1.321508      .82442
         pid |   .6886727   .4570774     1.51   0.132    -.2071826    1.584528
      libcon |  -.5977616    .479043    -1.25   0.212    -1.536669    .3411456
      gender |   .1082129   .2601412     0.42   0.677    -.4016545    .6180803
        educ |   .6567665   .2625849     2.50   0.012     .1421097    1.171423
    smonitor |  -1.336311   .6396135    -2.09   0.037     -2.58993   -.0826917
         age |   .0094026   .0079035     1.19   0.234    -.0060881    .0248932
         dif |  -.0513935   .6921822    -0.07   0.941    -1.408046    1.305259
             |
  c.smonitor#|
       c.dif |   6.895802   3.092575     2.23   0.026     .8344661    12.95714
             |
       _cons |  -1.352386   .2386408    -5.67   0.000    -1.820113   -.8846581
------------------------------------------------------------------------------

. 
. margins, at((mean) _all gender=1 educ=0 (p10) dif (min) smonitor) at((mean) _
> all gender=1 educ=0 (p10) dif (max) smonitor) at((mean) _all gender=1 educ=0 
> (p90) dif (min) smonitor) at((mean) _all gender=1 educ=0 (p90) dif (max) smon
> itor) post

Adjusted predictions                            Number of obs     =        707
Model VCE    : OIM

1._predict   : Pr(mid2==midpoint), predict(pr outcome(1))
2._predict   : Pr(mid2==endorse), predict(pr outcome(2))
3._predict   : Pr(mid2==reject), predict(pr outcome(3))
4._predict   : Pr(mid2==opt_out), predict(pr outcome(4))

1._at        : egalitaria~m    =    .0008337 (mean)
               individual~m    =    .0001747 (mean)
               pid             =   -.0003922 (mean)
               libcon          =   -.0027522 (mean)
               gender          =           1
               educ            =           0
               smonitor        =   -.2919736 (min)
               age             =   -.2838429 (mean)
               dif             =   -.1351765 (p10)

2._at        : egalitaria~m    =    .0008337 (mean)
               individual~m    =    .0001747 (mean)
               pid             =   -.0003922 (mean)
               libcon          =   -.0027522 (mean)
               gender          =           1
               educ            =           0
               smonitor        =    .7080264 (max)
               age             =   -.2838429 (mean)
               dif             =   -.1351765 (p10)

3._at        : egalitaria~m    =    .0008337 (mean)
               individual~m    =    .0001747 (mean)
               pid             =   -.0003922 (mean)
               libcon          =   -.0027522 (mean)
               gender          =           1
               educ            =           0
               smonitor        =   -.2919736 (min)
               age             =   -.2838429 (mean)
               dif             =    .2360098 (p90)

4._at        : egalitaria~m    =    .0008337 (mean)
               individual~m    =    .0001747 (mean)
               pid             =   -.0003922 (mean)
               libcon          =   -.0027522 (mean)
               gender          =           1
               educ            =           0
               smonitor        =    .7080264 (max)
               age             =   -.2838429 (mean)
               dif             =    .2360098 (p90)

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_predict#_at |
        1 1  |   .3817995   .0471705     8.09   0.000      .289347    .4742521
        1 2  |    .376765   .0887632     4.24   0.000     .2027923    .5507376
        1 3  |   .2864298   .0600294     4.77   0.000     .1687743    .4040852
        1 4  |   .3710703   .1082439     3.43   0.001     .1589161    .5832245
        2 1  |   .2557099   .0391858     6.53   0.000     .1789072    .3325127
        2 2  |   .4692736    .093825     5.00   0.000       .28538    .6531673
        2 3  |   .4966826   .0683631     7.27   0.000     .3626933    .6306718
        2 4  |   .2459555   .0904912     2.72   0.007      .068596     .423315
        3 1  |   .2186123   .0400536     5.46   0.000     .1401087     .297116
        3 2  |   .1266406   .0469704     2.70   0.007     .0345804    .2187009
        3 3  |    .087028   .0306202     2.84   0.004     .0270136    .1470424
        3 4  |   .2969336   .0982999     3.02   0.003     .1042692    .4895979
        4 1  |   .1438782   .0346506     4.15   0.000     .0759642    .2117922
        4 2  |   .0273208   .0160442     1.70   0.089    -.0041253    .0587669
        4 3  |   .1298596   .0415868     3.12   0.002     .0483511    .2113682
        4 4  |   .0860406   .0453754     1.90   0.058    -.0028934    .1749747
------------------------------------------------------------------------------

. 
. /* Difference in predicted probabilities for midpoint for high and low self-m
> onitors in diverse contexts -- p.18 of the supplement */
. 
. lincom _b[1bn._predict#4._at] - _b[1bn._predict#3._at]

 ( 1)  - 1bn._predict#3._at + 1bn._predict#4._at = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0846406    .144533     0.59   0.558     -.198639    .3679201
------------------------------------------------------------------------------

. lincom _b[1bn._predict#2._at] - _b[1bn._predict#1._at]

 ( 1)  - 1bn._predict#1bn._at + 1bn._predict#2._at = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0050346    .114187    -0.04   0.965    -.2288369    .2187678
------------------------------------------------------------------------------

. 
. /* Difference in predicted probabilities for midpoint low self-monitors in lo
> w and highly diverse contexts -- p.19 of the supplement */
. 
. lincom _b[1bn._predict#1._at] - _b[1bn._predict#3._at]

 ( 1)  1bn._predict#1bn._at - 1bn._predict#3._at = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0953697    .071077     1.34   0.180    -.0439386    .2346781
------------------------------------------------------------------------------

. 
. lincom (_b[1bn._predict#4._at] - _b[1bn._predict#3._at]) - (_b[1bn._predict#2
> ._at] - _b[1bn._predict#1._at])

 ( 1)  1bn._predict#1bn._at - 1bn._predict#2._at - 1bn._predict#3._at +
       1bn._predict#4._at = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0896751   .1853121     0.48   0.628      -.27353    .4528802
------------------------------------------------------------------------------

. 
. lincom (_b[1bn._predict#4._at] - _b[2._predict#4._at]) - (_b[1bn._predict#3._
> at] - _b[2._predict#3._at])

 ( 1)  - 1bn._predict#3._at + 1bn._predict#4._at + 2._predict#3._at -
       2._predict#4._at = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .3353676   .2472881     1.36   0.175    -.1493081    .8200433
------------------------------------------------------------------------------

. 
. lincom _b[2._predict#4._at] - _b[2._predict#3._at]

 ( 1)  - 2._predict#3._at + 2._predict#4._at = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.2507271   .1358564    -1.85   0.065    -.5170006    .0155465
------------------------------------------------------------------------------

. lincom _b[2._predict#2._at] - _b[2._predict#1._at]

 ( 1)  - 2._predict#1bn._at + 2._predict#2._at = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .2135637   .1143721     1.87   0.062    -.0106015    .4377288
------------------------------------------------------------------------------

. 
. lincom (_b[2._predict#4._at] - _b[2._predict#3._at]) - (_b[2._predict#2._at] 
> - _b[2._predict#1._at])

 ( 1)  2._predict#1bn._at - 2._predict#2._at - 2._predict#3._at +
       2._predict#4._at = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.4642908   .1808474    -2.57   0.010    -.8187451   -.1098364
------------------------------------------------------------------------------

. 
. lincom _b[3._predict#4._at] - _b[3._predict#3._at]

 ( 1)  - 3._predict#3._at + 3._predict#4._at = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .2099055   .1132502     1.85   0.064    -.0120608    .4318719
------------------------------------------------------------------------------

. lincom _b[3._predict#2._at] - _b[3._predict#1._at]

 ( 1)  - 3._predict#1bn._at + 3._predict#2._at = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0919717   .0700614    -1.31   0.189    -.2292895    .0453461
------------------------------------------------------------------------------

. 
. /* Difference in predicted probabilities for opt-out low self-monitors in low
>  and highly diverse contexts -- p.19 of the supplement */
. 
. lincom _b[4._predict#4._at] - _b[4._predict#3._at]

 ( 1)  - 4._predict#3._at + 4._predict#4._at = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   -.043819    .071869    -0.61   0.542    -.1846797    .0970416
------------------------------------------------------------------------------

. lincom _b[4._predict#2._at] - _b[4._predict#1._at]

 ( 1)  - 4._predict#1bn._at + 4._predict#2._at = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.1165574   .0424058    -2.75   0.006    -.1996713   -.0334436
------------------------------------------------------------------------------

. 
. lincom (_b[4._predict#4._at] - _b[4._predict#3._at]) - (_b[4._predict#2._at] 
> - _b[4._predict#1._at])

 ( 1)  4._predict#1bn._at - 4._predict#2._at - 4._predict#3._at +
       4._predict#4._at = 0

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0727384   .0782711     0.93   0.353    -.0806701    .2261469
------------------------------------------------------------------------------

. 
. mlogit mid2 egalitarianism individualism   pid libcon gender educ smonitor ag
> e  dif c.smonitor#c.dif, base(2)

Iteration 0:   log likelihood = -939.15682  
Iteration 1:   log likelihood = -903.82669  
Iteration 2:   log likelihood = -903.05897  
Iteration 3:   log likelihood = -903.05624  
Iteration 4:   log likelihood = -903.05624  

Multinomial logistic regression                 Number of obs     =        707
                                                LR chi2(30)       =      72.20
                                                Prob > chi2       =     0.0000
Log likelihood = -903.05624                     Pseudo R2         =     0.0384

------------------------------------------------------------------------------
        mid2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
midpoint     |
egalitaria~m |   .4164519   .3701857     1.12   0.261    -.3090988    1.142003
individual~m |  -.1095046   .4058176    -0.27   0.787    -.9048924    .6858832
         pid |    .280696   .3339303     0.84   0.401    -.3737955    .9351874
      libcon |  -.0746433   .3516159    -0.21   0.832    -.7637978    .6145112
      gender |   .3551702   .1964073     1.81   0.071    -.0297809    .7401214
        educ |   .2964652    .197825     1.50   0.134    -.0912646    .6841951
    smonitor |  -.0442507   .4419366    -0.10   0.920    -.9104305    .8219291
         age |   .0000225   .0058725     0.00   0.997    -.0114874    .0115324
         dif |  -1.318404   .5662597    -2.33   0.020    -2.428253   -.2085559
             |
  c.smonitor#|
       c.dif |   4.262321   2.590878     1.65   0.100    -.8157062    9.340347
             |
       _cons |   -.314098   .1711929    -1.83   0.067    -.6496298    .0214339
-------------+----------------------------------------------------------------
endorse      |  (base outcome)
-------------+----------------------------------------------------------------
reject       |
egalitaria~m |  -.0481369   .4202915    -0.11   0.909    -.8718931    .7756193
individual~m |   .4121302   .4680687     0.88   0.379    -.5052676    1.329528
         pid |   .4984703   .3819747     1.30   0.192    -.2501864    1.247127
      libcon |  -.9311416   .3980857    -2.34   0.019    -1.711375   -.1509079
      gender |   .0493415   .2228306     0.22   0.825    -.3873984    .4860815
        educ |   .5842171   .2263391     2.58   0.010     .1406006    1.027834
    smonitor |  -.0302785   .5038566    -0.06   0.952    -1.017819    .9572623
         age |   .0171665    .006649     2.58   0.010     .0041346    .0301983
         dif |  -1.844824   .7456188    -2.47   0.013     -3.30621   -.3834383
             |
  c.smonitor#|
       c.dif |   8.306253   2.788529     2.98   0.003     2.840837    13.77167
             |
       _cons |  -.7896602   .1976756    -3.99   0.000    -1.177097   -.4022233
-------------+----------------------------------------------------------------
opt_out      |
egalitaria~m |   1.385517   .5118665     2.71   0.007      .382277    2.388757
individual~m |  -.2485439   .5474406    -0.45   0.650    -1.321508      .82442
         pid |   .6886727   .4570774     1.51   0.132    -.2071826    1.584528
      libcon |  -.5977616    .479043    -1.25   0.212    -1.536669    .3411456
      gender |   .1082129   .2601412     0.42   0.677    -.4016545    .6180803
        educ |   .6567665   .2625849     2.50   0.012     .1421097    1.171423
    smonitor |  -1.336311   .6396135    -2.09   0.037     -2.58993   -.0826917
         age |   .0094026   .0079035     1.19   0.234    -.0060881    .0248932
         dif |  -.0513935   .6921822    -0.07   0.941    -1.408046    1.305259
             |
  c.smonitor#|
       c.dif |   6.895802   3.092575     2.23   0.026     .8344661    12.95714
             |
       _cons |  -1.352386   .2386408    -5.67   0.000    -1.820113   -.8846581
------------------------------------------------------------------------------

. 
. margins, at((mean) _all dif=0 smonitor=(0 1)) at((mean) _all dif=1 smonitor=(
> 0 1)) post

Adjusted predictions                            Number of obs     =        707
Model VCE    : OIM

1._predict   : Pr(mid2==midpoint), predict(pr outcome(1))
2._predict   : Pr(mid2==endorse), predict(pr outcome(2))
3._predict   : Pr(mid2==reject), predict(pr outcome(3))
4._predict   : Pr(mid2==opt_out), predict(pr outcome(4))

1._at        : egalitaria~m    =    .0008337 (mean)
               individual~m    =    .0001747 (mean)
               pid             =   -.0003922 (mean)
               libcon          =   -.0027522 (mean)
               gender          =    .5601132 (mean)
               educ            =    .4554455 (mean)
               smonitor        =           0
               age             =   -.2838429 (mean)
               dif             =           0

2._at        : egalitaria~m    =    .0008337 (mean)
               individual~m    =    .0001747 (mean)
               pid             =   -.0003922 (mean)
               libcon          =   -.0027522 (mean)
               gender          =    .5601132 (mean)
               educ            =    .4554455 (mean)
               smonitor        =           1
               age             =   -.2838429 (mean)
               dif             =           0

3._at        : egalitaria~m    =    .0008337 (mean)
               individual~m    =    .0001747 (mean)
               pid             =   -.0003922 (mean)
               libcon          =   -.0027522 (mean)
               gender          =    .5601132 (mean)
               educ            =    .4554455 (mean)
               smonitor        =           0
               age             =   -.2838429 (mean)
               dif             =           1

4._at        : egalitaria~m    =    .0008337 (mean)
               individual~m    =    .0001747 (mean)
               pid             =   -.0003922 (mean)
               libcon          =   -.0027522 (mean)
               gender          =    .5601132 (mean)
               educ            =    .4554455 (mean)
               smonitor        =           1
               age             =   -.2838429 (mean)
               dif             =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_predict#_at |
        1 1  |   .3403318   .0186448    18.25   0.000     .3037887    .3768749
        1 2  |   .3666125   .0919767     3.99   0.000     .1863415    .5468835
        1 3  |   .1586516   .0716382     2.21   0.027     .0182434    .2990598
        1 4  |   .0379825   .0873891     0.43   0.664    -.1332969     .209262
        2 1  |   .3334999   .0186039    17.93   0.000     .2970371    .3699628
        2 2  |   .3755073   .0931387     4.03   0.000     .1929587    .5580558
        2 3  |   .5810501   .1219441     4.76   0.000     .3420441    .8200561
        2 4  |   .0020487   .0051251     0.40   0.689    -.0079963    .0120936
        3 1  |   .2025982   .0161341    12.56   0.000     .1709759    .2342205
        3 2  |   .2213138   .0784657     2.82   0.005     .0675238    .3751037
        3 3  |     .05579   .0383473     1.45   0.146    -.0193694    .1309494
        3 4  |   .7727187   .4444908     1.74   0.082    -.0984672    1.643905
        4 1  |     .12357   .0134679     9.18   0.000     .0971734    .1499667
        4 2  |   .0365665   .0220385     1.66   0.097    -.0066281    .0797611
        4 3  |   .2045083   .1064903     1.92   0.055    -.0042088    .4132254
        4 4  |   .1872501   .4253661     0.44   0.660    -.6464522    1.020952
------------------------------------------------------------------------------

. 
. nlcom ln((_b[1bn._predict#4._at]/_b[2._predict#4._at])/(_b[1bn._predict#3._at
> ]/_b[2._predict#3._at]))

       _nl_1:  ln((_b[1bn._predict#4._at]/_b[2._predict#4._at])/(_b[1bn._predic
> t#3._at]/_b[2._predict#3._at]))

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _nl_1 |    4.21807    2.64387     1.60   0.111    -.9638206     9.39996
------------------------------------------------------------------------------

. 
. egen smonitor_min=min(smonitor)

. 
. replace smonitor=smonitor-smonitor_min
(745 real changes made)

. 
. label variable smonitor "Self-Monitoring"

. 
. mlogit mid2 egalitarianism individualism pid libcon gender educ smonitor age 
> dif c.smonitor#c.dif, base(2)

Iteration 0:   log likelihood = -939.15682  
Iteration 1:   log likelihood = -903.82669  
Iteration 2:   log likelihood = -903.05897  
Iteration 3:   log likelihood = -903.05624  
Iteration 4:   log likelihood = -903.05624  

Multinomial logistic regression                 Number of obs     =        707
                                                LR chi2(30)       =      72.20
                                                Prob > chi2       =     0.0000
Log likelihood = -903.05624                     Pseudo R2         =     0.0384

------------------------------------------------------------------------------
        mid2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
midpoint     |
egalitaria~m |   .4164519   .3701857     1.12   0.261    -.3090988    1.142003
individual~m |  -.1095046   .4058176    -0.27   0.787    -.9048924    .6858832
         pid |    .280696   .3339303     0.84   0.401    -.3737955    .9351874
      libcon |  -.0746433   .3516159    -0.21   0.832    -.7637978    .6145112
      gender |   .3551702   .1964073     1.81   0.071    -.0297809    .7401214
        educ |   .2964652    .197825     1.50   0.134    -.0912646    .6841951
    smonitor |  -.0442507   .4419366    -0.10   0.920    -.9104305    .8219291
         age |   .0000225   .0058725     0.00   0.997    -.0114874    .0115324
         dif |   -2.56289   .9806379    -2.61   0.009    -4.484905   -.6408746
             |
  c.smonitor#|
       c.dif |   4.262321   2.590878     1.65   0.100    -.8157062    9.340347
             |
       _cons |  -.3011779   .2162531    -1.39   0.164    -.7250262    .1226704
-------------+----------------------------------------------------------------
endorse      |  (base outcome)
-------------+----------------------------------------------------------------
reject       |
egalitaria~m |  -.0481369   .4202915    -0.11   0.909    -.8718931    .7756193
individual~m |   .4121302   .4680687     0.88   0.379    -.5052676    1.329528
         pid |   .4984703   .3819747     1.30   0.192    -.2501864    1.247127
      libcon |  -.9311416   .3980857    -2.34   0.019    -1.711375   -.1509079
      gender |   .0493415   .2228306     0.22   0.825    -.3873984    .4860815
        educ |   .5842171   .2263391     2.58   0.010     .1406006    1.027834
    smonitor |  -.0302785   .5038566    -0.06   0.952    -1.017819    .9572623
         age |   .0171665    .006649     2.58   0.010     .0041346    .0301983
         dif |  -4.270031   1.258214    -3.39   0.001    -6.736086   -1.803977
             |
  c.smonitor#|
       c.dif |   8.306253   2.788529     2.98   0.003     2.840837    13.77167
             |
       _cons |  -.7808197   .2497318    -3.13   0.002    -1.270285   -.2913544
-------------+----------------------------------------------------------------
opt_out      |
egalitaria~m |   1.385517   .5118665     2.71   0.007      .382277    2.388757
individual~m |  -.2485439   .5474406    -0.45   0.650    -1.321508      .82442
         pid |   .6886727   .4570774     1.51   0.132    -.2071826    1.584528
      libcon |  -.5977616    .479043    -1.25   0.212    -1.536669    .3411456
      gender |   .1082129   .2601412     0.42   0.677    -.4016545    .6180803
        educ |   .6567665   .2625849     2.50   0.012     .1421097    1.171423
    smonitor |  -1.336311   .6396135    -2.09   0.037    -2.589931   -.0826917
         age |   .0094026   .0079035     1.19   0.234    -.0060881    .0248932
         dif |  -2.064786   1.171964    -1.76   0.078    -4.361792    .2322205
             |
  c.smonitor#|
       c.dif |   6.895802   3.092575     2.23   0.026     .8344661    12.95714
             |
       _cons |   -.962218   .2838954    -3.39   0.001    -1.518643   -.4057932
------------------------------------------------------------------------------

. 
. /* Create Figure A.1 (b) */
. 
. margins, at((mean) _all gender=1 educ=0 (p90) dif smonitor=(0(.2)1)) predict(
> outcome(1)) post

Adjusted predictions                            Number of obs     =        707
Model VCE    : OIM

Expression   : Pr(mid2==midpoint), predict(outcome(1))

1._at        : egalitaria~m    =    .0008337 (mean)
               individual~m    =    .0001747 (mean)
               pid             =   -.0003922 (mean)
               libcon          =   -.0027522 (mean)
               gender          =           1
               educ            =           0
               smonitor        =           0
               age             =   -.2838429 (mean)
               dif             =    .2360098 (p90)

2._at        : egalitaria~m    =    .0008337 (mean)
               individual~m    =    .0001747 (mean)
               pid             =   -.0003922 (mean)
               libcon          =   -.0027522 (mean)
               gender          =           1
               educ            =           0
               smonitor        =          .2
               age             =   -.2838429 (mean)
               dif             =    .2360098 (p90)

3._at        : egalitaria~m    =    .0008337 (mean)
               individual~m    =    .0001747 (mean)
               pid             =   -.0003922 (mean)
               libcon          =   -.0027522 (mean)
               gender          =           1
               educ            =           0
               smonitor        =          .4
               age             =   -.2838429 (mean)
               dif             =    .2360098 (p90)

4._at        : egalitaria~m    =    .0008337 (mean)
               individual~m    =    .0001747 (mean)
               pid             =   -.0003922 (mean)
               libcon          =   -.0027522 (mean)
               gender          =           1
               educ            =           0
               smonitor        =          .6
               age             =   -.2838429 (mean)
               dif             =    .2360098 (p90)

5._at        : egalitaria~m    =    .0008337 (mean)
               individual~m    =    .0001747 (mean)
               pid             =   -.0003922 (mean)
               libcon          =   -.0027522 (mean)
               gender          =           1
               educ            =           0
               smonitor        =          .8
               age             =   -.2838429 (mean)
               dif             =    .2360098 (p90)

6._at        : egalitaria~m    =    .0008337 (mean)
               individual~m    =    .0001747 (mean)
               pid             =   -.0003922 (mean)
               libcon          =   -.0027522 (mean)
               gender          =           1
               educ            =           0
               smonitor        =           1
               age             =   -.2838429 (mean)
               dif             =    .2360098 (p90)

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .2864298   .0600294     4.77   0.000     .1687743    .4040852
          2  |   .3129032   .0448121     6.98   0.000     .2250731    .4007334
          3  |   .3361795    .042963     7.82   0.000     .2519736    .4203854
          4  |   .3545832   .0577966     6.14   0.000     .2413039    .4678625
          5  |   .3665876    .081162     4.52   0.000      .207513    .5256622
          6  |   .3710703   .1082439     3.43   0.001     .1589161    .5832245
------------------------------------------------------------------------------

. 
. marginsplot, xdimension(smonitor) title(Violent Stereotype--High Diversity) y
> title(Predicted Probability) plot1opts(lpattern(dash) msymbol(O)) recastci(ra
> rea) yscale(range(0.15 0.6))

  Variables that uniquely identify margins: smonitor

. 
. graph export figures/figureA1b.eps, as(eps) replace
(note: file figures/figureA1b.eps not found)
(file figures/figureA1b.eps written in EPS format)

. /* Delete next two lines if not using linux */
. !epstopdf /figures/figureA1b.eps -o=figures/figureA1b.pdf


. !rm -f vshd.eps


. 
. mlogit mid2 egalitarianism individualism pid libcon gender educ smonitor age 
> dif c.smonitor#c.dif, base(2)

Iteration 0:   log likelihood = -939.15682  
Iteration 1:   log likelihood = -903.82669  
Iteration 2:   log likelihood = -903.05897  
Iteration 3:   log likelihood = -903.05624  
Iteration 4:   log likelihood = -903.05624  

Multinomial logistic regression                 Number of obs     =        707
                                                LR chi2(30)       =      72.20
                                                Prob > chi2       =     0.0000
Log likelihood = -903.05624                     Pseudo R2         =     0.0384

------------------------------------------------------------------------------
        mid2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
midpoint     |
egalitaria~m |   .4164519   .3701857     1.12   0.261    -.3090988    1.142003
individual~m |  -.1095046   .4058176    -0.27   0.787    -.9048924    .6858832
         pid |    .280696   .3339303     0.84   0.401    -.3737955    .9351874
      libcon |  -.0746433   .3516159    -0.21   0.832    -.7637978    .6145112
      gender |   .3551702   .1964073     1.81   0.071    -.0297809    .7401214
        educ |   .2964652    .197825     1.50   0.134    -.0912646    .6841951
    smonitor |  -.0442507   .4419366    -0.10   0.920    -.9104305    .8219291
         age |   .0000225   .0058725     0.00   0.997    -.0114874    .0115324
         dif |   -2.56289   .9806379    -2.61   0.009    -4.484905   -.6408746
             |
  c.smonitor#|
       c.dif |   4.262321   2.590878     1.65   0.100    -.8157062    9.340347
             |
       _cons |  -.3011779   .2162531    -1.39   0.164    -.7250262    .1226704
-------------+----------------------------------------------------------------
endorse      |  (base outcome)
-------------+----------------------------------------------------------------
reject       |
egalitaria~m |  -.0481369   .4202915    -0.11   0.909    -.8718931    .7756193
individual~m |   .4121302   .4680687     0.88   0.379    -.5052676    1.329528
         pid |   .4984703   .3819747     1.30   0.192    -.2501864    1.247127
      libcon |  -.9311416   .3980857    -2.34   0.019    -1.711375   -.1509079
      gender |   .0493415   .2228306     0.22   0.825    -.3873984    .4860815
        educ |   .5842171   .2263391     2.58   0.010     .1406006    1.027834
    smonitor |  -.0302785   .5038566    -0.06   0.952    -1.017819    .9572623
         age |   .0171665    .006649     2.58   0.010     .0041346    .0301983
         dif |  -4.270031   1.258214    -3.39   0.001    -6.736086   -1.803977
             |
  c.smonitor#|
       c.dif |   8.306253   2.788529     2.98   0.003     2.840837    13.77167
             |
       _cons |  -.7808197   .2497318    -3.13   0.002    -1.270285   -.2913544
-------------+----------------------------------------------------------------
opt_out      |
egalitaria~m |   1.385517   .5118665     2.71   0.007      .382277    2.388757
individual~m |  -.2485439   .5474406    -0.45   0.650    -1.321508      .82442
         pid |   .6886727   .4570774     1.51   0.132    -.2071826    1.584528
      libcon |  -.5977616    .479043    -1.25   0.212    -1.536669    .3411456
      gender |   .1082129   .2601412     0.42   0.677    -.4016545    .6180803
        educ |   .6567665   .2625849     2.50   0.012     .1421097    1.171423
    smonitor |  -1.336311   .6396135    -2.09   0.037    -2.589931   -.0826917
         age |   .0094026   .0079035     1.19   0.234    -.0060881    .0248932
         dif |  -2.064786   1.171964    -1.76   0.078    -4.361792    .2322205
             |
  c.smonitor#|
       c.dif |   6.895802   3.092575     2.23   0.026     .8344661    12.95714
             |
       _cons |   -.962218   .2838954    -3.39   0.001    -1.518643   -.4057932
------------------------------------------------------------------------------

. 
. /* Create Figure A.1 (a) */
. 
. margins, at((mean) _all gender=1 educ=0 (p10) dif smonitor=(0(.2)1)) predict(
> outcome(1)) post

Adjusted predictions                            Number of obs     =        707
Model VCE    : OIM

Expression   : Pr(mid2==midpoint), predict(outcome(1))

1._at        : egalitaria~m    =    .0008337 (mean)
               individual~m    =    .0001747 (mean)
               pid             =   -.0003922 (mean)
               libcon          =   -.0027522 (mean)
               gender          =           1
               educ            =           0
               smonitor        =           0
               age             =   -.2838429 (mean)
               dif             =   -.1351765 (p10)

2._at        : egalitaria~m    =    .0008337 (mean)
               individual~m    =    .0001747 (mean)
               pid             =   -.0003922 (mean)
               libcon          =   -.0027522 (mean)
               gender          =           1
               educ            =           0
               smonitor        =          .2
               age             =   -.2838429 (mean)
               dif             =   -.1351765 (p10)

3._at        : egalitaria~m    =    .0008337 (mean)
               individual~m    =    .0001747 (mean)
               pid             =   -.0003922 (mean)
               libcon          =   -.0027522 (mean)
               gender          =           1
               educ            =           0
               smonitor        =          .4
               age             =   -.2838429 (mean)
               dif             =   -.1351765 (p10)

4._at        : egalitaria~m    =    .0008337 (mean)
               individual~m    =    .0001747 (mean)
               pid             =   -.0003922 (mean)
               libcon          =   -.0027522 (mean)
               gender          =           1
               educ            =           0
               smonitor        =          .6
               age             =   -.2838429 (mean)
               dif             =   -.1351765 (p10)

5._at        : egalitaria~m    =    .0008337 (mean)
               individual~m    =    .0001747 (mean)
               pid             =   -.0003922 (mean)
               libcon          =   -.0027522 (mean)
               gender          =           1
               educ            =           0
               smonitor        =          .8
               age             =   -.2838429 (mean)
               dif             =   -.1351765 (p10)

6._at        : egalitaria~m    =    .0008337 (mean)
               individual~m    =    .0001747 (mean)
               pid             =   -.0003922 (mean)
               libcon          =   -.0027522 (mean)
               gender          =           1
               educ            =           0
               smonitor        =           1
               age             =   -.2838429 (mean)
               dif             =   -.1351765 (p10)

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .3817995   .0471705     8.09   0.000      .289347    .4742521
          2  |   .3930852   .0354913    11.08   0.000     .3235235    .4626469
          3  |   .3974517   .0359888    11.04   0.000      .326915    .4679884
          4  |   .3955791   .0487868     8.11   0.000     .2999587    .4911995
          5  |   .3883607   .0676133     5.74   0.000     .2558409    .5208804
          6  |    .376765   .0887632     4.24   0.000     .2027923    .5507376
------------------------------------------------------------------------------

. 
. marginsplot, xdimension(smonitor) title(Violent Stereotype--Low Diversity) yt
> itle(Predicted Probability) plot1opts(lpattern(dash) msymbol(O)) recastci(rar
> ea) yscale(range(0.15 0.6))

  Variables that uniquely identify margins: smonitor

. 
. graph export figures/figureA1a.eps, as(eps) replace
(note: file figures/figureA1a.eps not found)
(file figures/figureA1a.eps written in EPS format)

. /* Delete next two lines if not using linux */
. !epstopdf figures/figureA1a.eps -o=figures/figureA1avsld.pdf


. !rm -f vsld.eps


. 
. mlogit mid2 egalitarianism individualism pid libcon gender educ smonitor age 
> dif c.smonitor#c.dif, base(3)

Iteration 0:   log likelihood = -939.15682  
Iteration 1:   log likelihood = -903.82669  
Iteration 2:   log likelihood = -903.05897  
Iteration 3:   log likelihood = -903.05624  
Iteration 4:   log likelihood = -903.05624  

Multinomial logistic regression                 Number of obs     =        707
                                                LR chi2(30)       =      72.20
                                                Prob > chi2       =     0.0000
Log likelihood = -903.05624                     Pseudo R2         =     0.0384

------------------------------------------------------------------------------
        mid2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
midpoint     |
egalitaria~m |   .4645888   .4168993     1.11   0.265    -.3525189    1.281696
individual~m |  -.5216348   .4638556    -1.12   0.261    -1.430775    .3875055
         pid |  -.2177743   .3791226    -0.57   0.566     -.960841    .5252924
      libcon |   .8564983   .3966771     2.16   0.031     .0790255    1.633971
      gender |   .3058287   .2221318     1.38   0.169    -.1295415     .741199
        educ |  -.2877518   .2224372    -1.29   0.196    -.7237207    .1482171
    smonitor |  -.0139722    .502634    -0.03   0.978    -.9991168    .9711723
         age |  -.0171439   .0066063    -2.60   0.009     -.030092   -.0041959
         dif |   1.707142   1.293397     1.32   0.187    -.8278708    4.242154
             |
  c.smonitor#|
       c.dif |  -4.043933   2.683102    -1.51   0.132    -9.302716    1.214851
             |
       _cons |   .4796418   .2529146     1.90   0.058    -.0160617    .9753452
-------------+----------------------------------------------------------------
endorse      |
egalitaria~m |   .0481369   .4202915     0.11   0.909    -.7756193    .8718931
individual~m |  -.4121302   .4680687    -0.88   0.379    -1.329528    .5052676
         pid |  -.4984703   .3819747    -1.30   0.192    -1.247127    .2501864
      libcon |   .9311416   .3980857     2.34   0.019     .1509079    1.711375
      gender |  -.0493415   .2228306    -0.22   0.825    -.4860815    .3873984
        educ |  -.5842171   .2263391    -2.58   0.010    -1.027834   -.1406006
    smonitor |   .0302785   .5038566     0.06   0.952    -.9572623    1.017819
         age |  -.0171665    .006649    -2.58   0.010    -.0301983   -.0041346
         dif |   4.270031   1.258214     3.39   0.001     1.803977    6.736086
             |
  c.smonitor#|
       c.dif |  -8.306253   2.788529    -2.98   0.003    -13.77167   -2.840837
             |
       _cons |   .7808197   .2497318     3.13   0.002     .2913544    1.270285
-------------+----------------------------------------------------------------
reject       |  (base outcome)
-------------+----------------------------------------------------------------
opt_out      |
egalitaria~m |   1.433654   .5436361     2.64   0.008     .3681466    2.499161
individual~m |  -.6606741   .5871897    -1.13   0.261    -1.811545    .4901965
         pid |   .1902024   .4867704     0.39   0.696    -.7638501    1.144255
      libcon |   .3333801   .5099059     0.65   0.513    -.6660172    1.332777
      gender |   .0588714   .2781432     0.21   0.832    -.4862792     .604022
        educ |   .0725495    .281009     0.26   0.796     -.478218    .6233169
    smonitor |  -1.306033   .6768534    -1.93   0.054    -2.632641    .0205757
         age |  -.0077639   .0083744    -0.93   0.354    -.0241775    .0086497
         dif |   2.205245   1.426709     1.55   0.122    -.5910529    5.001544
             |
  c.smonitor#|
       c.dif |  -1.410451   3.043703    -0.46   0.643       -7.376    4.555098
             |
       _cons |  -.1813983   .3113266    -0.58   0.560    -.7915872    .4287907
------------------------------------------------------------------------------

. 
. mlogit mid2 egalitarianism individualism pid libcon gender educ smonitor age 
> dif c.smonitor#c.dif, base(4)

Iteration 0:   log likelihood = -939.15682  
Iteration 1:   log likelihood = -903.82669  
Iteration 2:   log likelihood = -903.05897  
Iteration 3:   log likelihood = -903.05624  
Iteration 4:   log likelihood = -903.05624  

Multinomial logistic regression                 Number of obs     =        707
                                                LR chi2(30)       =      72.20
                                                Prob > chi2       =     0.0000
Log likelihood = -903.05624                     Pseudo R2         =     0.0384

------------------------------------------------------------------------------
        mid2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
midpoint     |
egalitaria~m |   -.969065   .5076246    -1.91   0.056    -1.963991    .0258608
individual~m |   .1390393   .5437404     0.26   0.798    -.9266723    1.204751
         pid |  -.4079768   .4535565    -0.90   0.368    -1.296931    .4809776
      libcon |   .5231183   .4764479     1.10   0.272    -.4107024    1.456939
      gender |   .2469573   .2593424     0.95   0.341    -.2613444    .7552591
        educ |  -.3603013   .2597396    -1.39   0.165    -.8693816     .148779
    smonitor |    1.29206   .6352962     2.03   0.042     .0469027    2.537218
         age |  -.0093801     .00784    -1.20   0.232    -.0247463    .0059861
         dif |  -.4981038   1.247529    -0.40   0.690    -2.943217    1.947009
             |
  c.smonitor#|
       c.dif |  -2.633481   3.105608    -0.85   0.396    -8.720361    3.453398
             |
       _cons |   .6610401   .2870555     2.30   0.021     .0984216    1.223659
-------------+----------------------------------------------------------------
endorse      |
egalitaria~m |  -1.385517   .5118665    -2.71   0.007    -2.388757    -.382277
individual~m |   .2485439   .5474406     0.45   0.650      -.82442    1.321508
         pid |  -.6886727   .4570774    -1.51   0.132    -1.584528    .2071826
      libcon |   .5977616    .479043     1.25   0.212    -.3411456    1.536669
      gender |  -.1082129   .2601412    -0.42   0.677    -.6180803    .4016545
        educ |  -.6567665   .2625849    -2.50   0.012    -1.171423   -.1421097
    smonitor |   1.336311   .6396135     2.09   0.037     .0826917    2.589931
         age |  -.0094026   .0079035    -1.19   0.234    -.0248932    .0060881
         dif |   2.064786   1.171964     1.76   0.078    -.2322205    4.361792
             |
  c.smonitor#|
       c.dif |  -6.895802   3.092575    -2.23   0.026    -12.95714   -.8344661
             |
       _cons |    .962218   .2838954     3.39   0.001     .4057932    1.518643
-------------+----------------------------------------------------------------
reject       |
egalitaria~m |  -1.433654   .5436361    -2.64   0.008    -2.499161   -.3681466
individual~m |   .6606741   .5871897     1.13   0.261    -.4901965    1.811545
         pid |  -.1902024   .4867704    -0.39   0.696    -1.144255    .7638501
      libcon |  -.3333801   .5099059    -0.65   0.513    -1.332777    .6660172
      gender |  -.0588714   .2781432    -0.21   0.832     -.604022    .4862792
        educ |  -.0725495    .281009    -0.26   0.796    -.6233169     .478218
    smonitor |   1.306033   .6768534     1.93   0.054    -.0205757    2.632641
         age |   .0077639   .0083744     0.93   0.354    -.0086497    .0241775
         dif |  -2.205245   1.426709    -1.55   0.122    -5.001544    .5910529
             |
  c.smonitor#|
       c.dif |   1.410451   3.043703     0.46   0.643    -4.555098       7.376
             |
       _cons |   .1813983   .3113266     0.58   0.560    -.4287907    .7915872
-------------+----------------------------------------------------------------
opt_out      |  (base outcome)
------------------------------------------------------------------------------

. 
. log close
      name:  <unnamed>
       log:  /home/ppaolino/research/projects/mlogit/polan/mlogit_replication.o
> ut
  log type:  text
 closed on:  10 Jul 2020, 11:20:52
-------------------------------------------------------------------------------
